N IST SP 1 2 3 0 - 2

Community Resilience-Focused Technical Investigation of the2016 LUMBERTON, NORTH CAROLINA FLOOD
COMMUNITY RECOVERY ONE YEAR LATEREDIT ORS
Elaina J. SutleyMaria K. Dillard
John W. van de Lindt
This publication is available free of charge from:https://doi.org/10.6028/NIST.SP.1230-2

NIST Special Publication 1230-2

Community Resilience-Focused Technical
Investigation of the 2016 Lumberton,
North Carolina Flood
Community Recovery One Year Later
Editors
Elaina J. Sutley
University of Kansas
Maria K. Dillard
National Institute of Standards and Technology
John W. van de Lindt
Colorado State University
This publication is available free of charge from:https://doi.org/10.6028/NIST.SP.1230-2
April 2021

U.S. Department of Commerce
Gina M. Raimondo, Secretary
National Institute of Standards and Technology
James K. Olthoff, Performing the Non-Exclusive Functions and Duties of the Under Secretary of Commerce
for Standards and Technology & Director, National Institute of Standards and Technology

Disclaimer No. 1
Certain commercial entities, equipment, or materials may be identified in this document in orderto describe an experimental procedure or concept adequately. Such identification is not intended
to imply recommendation or endorsement by the National Institute of Standards and Technology,
nor is it intended to imply that the entities, materials, or equipment are necessarily the best
available for the purpose.
Disclaimer No. 2
The information contained herein is provided as a public service with the understanding that
Colorado State University makes no warranties, either expressed or implied, concerning theaccuracy, completeness, reliability, or suitability of the information. Nor does Colorado State
University warrant that the use of this information is free of any claims of copyright
infringement.
Disclaimer No. 3
In compliance with the Paperwork Reduction Act, this information collection was completed
under the OMB Control number 0693-0078 with an expiration date of July 31, 2019.
Disclaimer No. 4
All maps in the report, except where noted, were created using ESRI ArcGIS.

2

Author Credits and Acknowledgements
Each section within each chapter of this report was prepared and edited by a number of different
authors from the field study team and leadership. In addition, the entire study team is grateful to
all those in and around Lumberton and within the State of North Carolina who shared theirknowledge and perspective through interviews and meetings with our researchers. The fieldstudy team acknowledges the generous contributions of many individuals in the public andprivate sectors to this study and associated reports.
Chapter 1 Authorship: Elaina Sutley, Maria Dillard, John van de Lindt, Walter Gillis Peacock,and Judith Mitrani-Reiser
Chapter 2 and associated Appendices Authorship: Elaina Sutley, Sara Hamideh, Maria Dillard,Donghwan Gu, with contributions from Walter Gillis Peacock, Nathanael Rosenheim, Shane
Crawford, Kijin Seong, and Shannon Van Zandt
Chapter 3 and associated Appendices Authorship: Maria Watson, Yu Xiao, Jennifer Helgeson,
Kai WuChapter 4 and associated Appendices Authorship: Maria Dillard, Jennifer Tobin, Ken Harrison,and John van de Lindt
Chapter 5 Authorship: John van de Lindt, Elaina Sutley, Maria Dillard
Appendices 1A-B and 5A-C Authorship: Field Study Team
Funding Acknowledgement
This research was conducted as part of the NIST Center of Excellence for Risk-Based
Community Resilience Planning under Cooperative Agreement 70NANB15H044 between the
National Institute of Standards and Technology (NIST) and Colorado State University. The
content expressed in this report are the views of the authors and do not necessarily represent theopinions or views of NIST or the U.S Department of Commerce.
Suggested Citation
Sutley, E.J., Dillard, M.K., and van de Lindt, J.W. (Eds.). (2021). Community ResilienceFocused Technical Investigation of the 2016 Lumberton, NC Flood: Community Recovery One
Year Later. NIST Special Publication 1230-2.

3

Field Study Team Members
The Lumberton Wave 2 Field Study Team consisted of 18 people. As described in this report
there were three major teams and many people participated on more than one team; theseincluded the Housing Team, the Business Team, and the Public Sector Team. Team affiliations
are listed as of the time of Wave 2 (January 2018).
Field Study Leadership
Elaina J. Sutley, University of Kansas
Maria K. Dillard, National Institute of Standards and Technology
John W. van de Lindt, Colorado State University
Field Study Team Members
Badeaux, Lauren, University of Kansas
Barbosa, Andre, Oregon State University
Crawford, Shane, University of Alabama
Dillard, Maria, National Institute of Standards and Technology
Gu, Donghwan, Texas A&M University
Hamideh, Sara, Iowa State University
Harrison, Kenneth, National Institute of Standards and Technology
Helgeson, Jennifer, National Institute of Standards and Technology
Khajehei, Sayma, Iowa State University
Koliou, Maria, Texas A&M University
Memari, Mehrdad, Colorado State University
Mitrani-Reiser, Judith, National Institute of Standards and Technology
Seong, Kijin, Texas A&M University
Sutley, Elaina, University of Kansas
Tobin, Jennifer, University of Colorado, Boulder
van de Lindt, John, Colorado State University
Watson, Maria, Texas A&M University
Wu, Kai, Texas A&M University
Other Contributors
Cutler, Harvey, Colorado State University
Farokhnia, Karim, Colorado State University
Fung, Juan, National Institute of Standards and Technology
Nofal, Omar, Colorado State University
Peacock, Walter, Texas A&M University
Peek, Lori, University of Colorado Boulder
Rosenheim, Nathanael, Texas A&M University
Tomiczek, Tori, U.S. Naval Academy
Van Zandt, Shannon, Texas A&M University
Xiao, Yu, Portland State University
Zahran, Sammy, Colorado State University

4

Executive Summary
In early October 2016 Hurricane Matthew crossed North Carolina as a category 1 storm withsome areas receiving 15 in. to 18 in. (38 cm to 46 cm) of rainfall on already saturated soil. The
NIST-funded Center for Risk-Based Community Resilience Planning (Center) teamed withresearchers from NIST’s Community Resilience, Disaster Failure Studies, and Applied
Economics programs to conduct a quick response field study focused on the small city ofLumberton, NC and the flooding experienced from the Lumber River. Approximately one year
later, the Center and NIST team returned to Lumberton to document and better understand thecommunity’s recovery progress with an emphasis on housing, businesses, schools, communityand state-level decisions, and the intersection of these sectors in community recovery. This typeof investigation is critical for the study of community resilience as it will ultimately provide
comprehensive longitudinal recovery data and analyses to support guidance andrecommendations on what is needed to afford communities the ability to recover more quickly
and equitably, and more generally, what attributes make most communities more resilient tonatural hazards.
This second in a series of community resilience-focused field studies is presented herein as Wave
2 of the on-going Lumberton, North Carolina Flood of 2016 report series. Recall that Wave 1
documented the initial physical and socio-economic impacts of the flooding on the community,
particularly for housing, households, schools, and other parts of the public sector. In January
2018, Wave 2 began the assessment of recovery with two major objectives: first, to document
community interdependencies1; and secondly, to document the point-in-time progress ofLumberton’s recovery. Wave 2 data collection dealt primarily with the recovery process of themost heavily-affected housing and businesses through two systematic surveys, as well asinterviews and meetings with select public officials. Analysis revealed that even after 14 months,
Lumberton was only in the early stages of recovery. Thus, recovery indicators will be used inWaves 3 and beyond to document the recovery trajectories for housing and businesses, as well as
provide multi-sector insight to the community’s recovery trajectory.
The team returned to the same housing units from Wave 1 to continue to assess recovery from
Hurricane Matthew. The Wave 2 housing surveys documented that 60 % of households whocompleted their survey reported being dislocated for at least one day, including 39 % dislocatedfor less than 30 days, 21% dislocated for more than 30 days, 11 % of households dislocated for
longer than one year, and 3 % reported themselves as still being dislocated. This does not include
the 26 % of the weighted sample that were abandoned residences. Given that surveys could not
be completed by these households, it is unclear when the household left and why they had not
returned. Only 64 % of households who completed the survey reported that their housing repairs
were complete. While recovery was still an active process, 90 % of surveyed households
reported that they planned to remain in their present housing unit for at least one year, and 91 %indicated that their access to essential needs, including work, school, and healthcare, had reachedpre-flood levels. Unlike Wave 1, the Wave 2 field study included questions about financing
repairs; 78 % of homeowners had homeowners’ insurance, 25 % had flood insurance, and 29 %of renters had renter’s insurance. Across participants with these three types of insurance, less1

Community interdependencies are defined here as the intersections of the population, housing, business, education,and healthcare sectors, utility and lifeline systems, and local governance.

5

than half received payouts. Approximately 50% of surveyed households applied for external
recovery resources, and slightly over half who applied received at least one type of financial
assistance.
A sample of businesses was added as part of Wave 2 to document business disruption andrecovery from Hurricane Matthew. Business surveys revealed that 45 % of those surveyedreported damage to their building, 51 % reported damage to building contents, and 28 % reported
damage to machinery. In total, 98 % lost power for an average of 10 days, and 88 % ceased
operation for at least one day. Nine in ten businesses experienced interruption in operations. At
the time of Wave 2, 24 % of businesses were operating below capacity while 34 % reported thatthey were in “survival mode”, “still recovering”, or “may never recover”.
During Wave 2 data collection, the team met with nine school district representatives including
one superintendent, two principals, two school counselors, three administrators, and one
custodial staff member. The team also met with four city representatives, including two
government officials, two infrastructure managers, and four state representatives—including
three emergency managers and one foundation officer representing the organization that was
selected to support distribution of federal funds. Interviews and subsequent virtual learning
uncovered that reconstruction on the primary wing of W.H. Knuckles Elementary went on for
nine months after the flooding, and ultimately, West Lumberton Elementary was permanently
closed. At the time of Wave 2, officials reported that they had lost over 500 students across the
district which they anticipated to have ripple effects on the level of federal funding. Much of thestudent loss was attributed to the loss of affordable housing, including public housing. School
counselors reported many students at elementary and high school levels still experiencing highlevels of anxiety during inclement weather.
The Lumberton water treatment plant (WTP) was operating largely as it had before the flooding
and water treatment plant operators had seen the water demand return to pre-event levels despitesome households having been permanently dislocated. Public Works was considering
floodproofing individual buildings (e.g., pumphouses) and generators, and installing a bermaround the WTP. At the broader community level, a flood gate at the VFW Road and CSX
Railroad underpass at I-95 was under consideration by the City, according to Public Worksrepresentatives. Public Works decided to pursue the construction of the WTP berm based on
assessments and recommendations from FEMA and with input of city officials. In addition to theberm and flood gate, water supply and stormwater management decisions were made to increase
the resilience of the local hospital.
Several major sources of federal disaster assistance had not reached Lumberton. This included
CDBG-DR funds through the Department of Housing and Urban Development (HUD). The
absence of these funds meant that several recovery activities at the community level could not
yet take place. One major exception was the FEMA public assistance program funds, which were
processed quickly, and distributed to the state and in turn to the local communities via theGolden LEAF Foundation.
Overall, the field team concludes that 14 months after the flooding, recovery was underway witha long road ahead for the people of Lumberton. The timing and breadth in the distribution of
6

financial recovery resources was identified as the primary factor delaying recovery progress.
This Wave 2 report summarizes the longitudinal study design and the results obtained throughthe housing and business surveys, as well as semi-structured public sector interviews andmeetings. Collectively, these findings quantitatively and qualitatively document the impacts andearly recovery process for the community of Lumberton, NC. The report provides conclusions
across all data collection efforts, as well as methodological considerations, and next steps for thelongitudinal study.

7

TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION.............................................................................................. 12
1.1 The NIST Center of Excellence for Risk-Based Community Resilience Planning ................ 12
1.2 The Engineering Laboratory at NIST ..................................................................................... 13
1.3 Hurricane Matthew and the 2016 (Wave 1) Field Study Overview ....................................... 15
1.4 Overview of the 2018 (Wave 2) Lumberton Field Study ....................................................... 17
1.5 The Scope and Audience for this Report ................................................................................ 18
CHAPTER 2: HOUSING DISRUPTION AND RECOVERY ............................................... 20
2.1 Goals and Objectives .............................................................................................................. 20
2.2 Sampling Procedure ................................................................................................................ 22
2.3 Survey Instrument ................................................................................................................... 23
2.4 Data Collection Methodology ................................................................................................. 26
2.4.1 Daily Operations .............................................................................................................. 26
2.4.2 Data Management ............................................................................................................ 27
2.5 Survey Results ........................................................................................................................ 28
2.5.1 Data Cleaning................................................................................................................... 29
2.5.2 Survey Response Rates .................................................................................................... 30
2.5.3 Findings: Flood Impacts and Recovery ........................................................................... 31
CHAPTER 3: BUSINESS INTERRUPTION AND RECOVERY......................................... 40
3.1 Goals and Objectives .............................................................................................................. 40
3.2 Sampling procedures ............................................................................................................... 41
3.3 Survey instrument ................................................................................................................... 43
3.4 Data Collection Methodology ................................................................................................. 46
3.4.1 Daily Operations .............................................................................................................. 47
3.4.2 Data Management ............................................................................................................ 51
3.4.3 Follow-up Work: Phone Calls and Determining Closure ................................................ 51
3.5 Survey results .......................................................................................................................... 52
3.5.1 Data cleaning ................................................................................................................... 52
3.5.2 Survey Response Rates .................................................................................................... 53
3.5.3 Findings: Flood Impacts and Recovery ........................................................................... 55
CHAPTER 4: PUBLIC SECTOR IMPACTS AND RECOVERY ........................................ 65
4.1 Introduction ............................................................................................................................. 65
4.2 Data Collection Methodology ................................................................................................. 65
4.3 Findings .................................................................................................................................. 68
4.3.1 Education ..................................................................................................................... 68
4.3.2 Water Infrastructure ..................................................................................................... 72
8

4.3.3 State and Local Government ........................................................................................ 75
CHAPTER 5: CONCLUSIONS AND NEXT STEPS ............................................................. 83
5.1 Conclusions for Housing Disruption and Recovery ............................................................... 83
5.2 Conclusions for Business Interruption and Recovery ............................................................. 84
5.3 Conclusions for Public Sector Impacts and Recovery ............................................................ 85
5.4 Concluding Methodological Considerations........................................................................... 85
5.5 Next Steps for the Lumberton Longitudinal Field Study........................................................ 86
REFERENCES............................................................................................................................ 88
APPENDICES .......................................................................................................................... A-1
Appendix 1B: Mental Health Resources..................................................................................... A-3
Appendix 2A: Housing Survey ................................................................................................... A-4
Appendix 2B: Residential Damage States and Income Levels ................................................... A-9
Appendix 2C: Housing Consent Script ..................................................................................... A-11
Appendix 2D: Description of External Recovery Funding Programs ...................................... A-13
Appendix 3A: Business Survey ................................................................................................ A-14
Appendix 3B: Business Damage States .................................................................................... A-19
Appendix 3C: Business Consent Script .................................................................................... A-20
Appendix 4A: Public Sector Consent Form.............................................................................. A-21
Appendix 4B: Semi-Structured Interview Guide for School Representatives.......................... A-24
Appendix 4C: Semi-Structured Interview Guide for Other Representatives............................ A-27
Appendix 5A: Technology in Field Studies.............................................................................. A-30
Appendix 5B: Safety Protocols................................................................................................. A-38
Appendix 5C: Training Protocol............................................................................................... A-41

9

LIST OF TABLES
Table 2-1. Housing Survey Completion Rates……………………...…………………...31
Table 2-2. Residential Building Damage………………………………………………..32
Table 2-3. Household Dislocation Duration Across Housing Type and Tenure…..……33
Table 2-4. Missed Work Due to Dislocation……………………………………………34
Table 2-5. External Influences on Return Decision-Making………………...………….35
Table 2-6. Progress towards Housing Recovery………………………………………...36
Table 2-7. Householder’s Perception of Children’s Educational Recovery………….....37
Table 2-8. Insurance Coverage and Claims Received..…………………………………37
Table 2-9. Recovery Assistance Application……..……………………………………..38
Table 2-10. Recovery Assistance Receipt and Timing………………………………….38
Table 2-11. Unmet Needs from Insurance and Recovery Assistance…………………...39
Table 2-12. Coverage from Unmet Needs from Insurance and Recovery Assistance…..39
Table 2-13. Race and Ethnicity of Survey Households.………………………………...39
Table 2-14. Maximum Household Education Level of Survey Households.…………...40
Table 2-15. Annual Household Income of Survey Households ………………………..40
Table 3-1. Sector Distribution of Sample……………………………………………….44
Table 3-2. Example of Team and Zone Assignments…………………………………...49
Table 3-3. Survey Codes used for In-Field Business Survey Coordination…………….50
Table 3-3. Sector Representation of Original Sample, Ineligible Business, and Final
Number of Eligible Businesses………………………………………………………….55
Table 3-4. Sector Representation by Data Collection and Operating Status……………56
Table 3-5. Extent of Damage……………………………………………………………57
Table 3-6. Utility Loss…………………………………………………………………..57
Table 3-7. Interruption by Damage……………………………………………………...59
Table 3-8. Utility Outage and Interruption in Undamaged Businesses…………………59
Table 3-9. Recovery Indicator: Perceived Recovery Status…………………………….60
Table 3-10. Recovery Indicator: Change in Revenue…………………………………...61
Table 3-11. Recovery by Days of Interruption………………………………………….61
Table 3-12. Interruption and Revenue…………………………………………………..62
Table 3-13. Recovery Status Change……………………………………………………63
Table 3-14. Insurance Coverage………………………………………………………...63
Table 3-15. Insurance Claims Received by Those Who Filed…………………………..63
Table 3-16. Number of Businesses Applying for External Assistance………………….64
Table 3-17. Number of Businesses that Received External Assistance out of Those that
Applied…………………………………………………………………………………..64
Table 3-18. Racial Makeup of the Respondents………………………………………...65
Table 3-19. Educational Attainment of the Respondents………………………………..65

10

LIST OF FIGURES
Figure 1-1. Path and Intensity Timeline of Hurricane Matthew………………….……..17
Figure 2-1. Sampled Housing Units……………………………………………………..24
Figure 2-2. Household Dislocation Time for all Responses ..…………………………..34
Figure 2-3. Number of Missed Work Days Due to Flood-Induced Housing Issues…….35
Figure 3-1. Business Sample Maps……………………………………………………...43
Figure 3-2. Business Outcome Conceptual Model………………………………………45
Figure 3-3. Field Map Example…………………………………………………………47
Figure 3-4. Process Flow for Data Collection used by the Teams………………………48
Figure 3-5. Frequency of Length of Interruption (Days)………………………………..58
Figure 3-6. Recovery Indicator: Current Operational Capacity…………………………60
Figure 3-7. Recovery Status by Revenue Change……………………………………….62

11

Chapter 1: Introduction
The longitudinal study of Lumberton, North Carolina described in this report is a collaboration
between researchers from the National Institute of Standards and Technology (NIST)-funded
Center of Excellence for Risk-Based Community Resilience Planning (Center), and researchers
in the Engineering Laboratory at NIST.

1.1 The NIST Center of Excellence for Risk-Based Community Resilience
Planning
Community resilience depends on the performance of the built environment and social,economic and public institutions which, individually and collectively, are essential for immediate
response and for long-term recovery of communities following a disaster. The interactions
between community needs and objectives, including post-disaster recovery goals, are not wellreflected in codes, standards, and other regulatory documents applied to the design of individual
facilities. This necessitates an approach which reflects the complex interdependencies among thephysical, social, and economic systems on which a healthy community depends. Thus, modelingthe resilience of communities to the impacts of natural hazards and disasters depends on many
disciplines, including engineering, social sciences, and information sciences. The Center,headquartered at Colorado State University in Fort Collins, Colorado and involving ten
additional universities at the time of Wave 2, was established by NIST in 2015. The multiuniversity Center’s overarching goal is to establish the measurement science for community
resilience assessment and risk-informed decision-making. To accomplish this goal, the Center isengaged in three major research thrusts aimed at: (1) developing a community resiliencemodeling environment – the “Interdependent Networked Community Resilience Modeling
Environment” or IN-CORE – to quantitatively assess alternative community resilience strategies,(2) developing a standardized data ontology, robust architecture, and management tools to
support IN-CORE, and (3) performing a comprehensive set of disaster hindcasts to validate INCORE’s advanced modeling environment. Data collection for a longitudinal field study isplanned and executed approximately every 12 months in the same location with the same sampleof housing units and businesses to support the following phases of resilience model development
within IN-CORE: impact, disruption, dislocation, recovery, and interdependency. The
Lumberton, NC field study will introduce comprehensive datasets under Thrust 2 to provide theinformation needed for validation of the full architecture in Thrust 3.
The Center works to accelerate the development of system-level models and databases that will
provide the technology for enhancing community resilience. Team members include noted
resilience experts from the University of Oklahoma, Oregon State University, Rice University,
Texas A&M University, the University of Illinois, the University of Washington, the Universityof South Alabama, California Polytechnic University-Pomona, the University of Kansas, andIowa State University. Ultimately, the decision framework created in the Center will provide
decision-makers with a unique set of tools that can be tailored to the needs of specificcommunities. These tools will optimize the design and subsequent management of individual
facilities and interdependent infrastructure systems to achieve resilience goals while managing
life-cycle costs. Its use will provide a basis for targeting public investments and incentives forprivate investments, thus making it possible to establish a “business case” for achieving
community resilience.
121.2 The Engineering Laboratory at NISTThe Engineering Laboratory (EL) at NIST promotes U.S. innovation and industrial
competitiveness by advancing measurement science, standards, and technology for engineered
systems in ways that enhance economic security and improve quality of life. Some of the ways
the EL carries out its mission is by undertaking activities in community resilience, disasters andbuilding failure investigations, economic analysis and life cycle assessment, wind and seismic
hazard impact reduction, fire prevention and control, engineering, and manufacturing materials.
Several researchers from the Applied Economics Office and the Community Resilience Programfrom the EL participated in the Lumberton field study to advance the disaster metrology researchof the EL’s Disaster and Failure Studies Program as well as to advance the measurement andmodeling work needed to support community resilience planning.
Hazard events test buildings and infrastructure in ways and on a scale that cannot be easily
replicated in a laboratory – buildings and infrastructure are built without being tested at fullscale. The study of disaster and failure events is essential to improving the performance ofbuildings and infrastructure, the safety of building occupants, and associated evacuation andemergency response procedures. NIST leads a multi-disciplinary Disaster and Failure Studies(DFS) Program within the Engineering Laboratory intended to standardize disaster field
deployment, assessment, and reporting protocols to ultimately improve building andinfrastructure performance. This program implements these goals through the following means:(1) monitoring events using a screening tool to evaluate whether decision criteria merits theestablishment and deployment of a study team, (2) coordinating the establishment, deployment,
operations and reporting of study teams, (3) ensuring that the study team’s safety, health andenvironmental requirements are met including relevant hazard reviews, training, and personal
protective equipment prior to deployment, (4) building and maintaining effective partnerships
and communications with other federal agencies, state/local governments, stakeholders and thegeneral public, (5) establishing and executes standard operating procedures and criteria fordisaster and failure studies, (6) promoting the implementation of recommendations from all DFS
investigations, (7) creating and maintaining an archival data repository for DFS, (8) carrying out
the statutory requirements of the National Construction Safety Team (NCST) Act, which
includes providing the Secretariat for the NCST Advisory Committee and annual reports toCongress, and (9) oversees a disaster metrology research program that interacts with othergroups in EL, to directly inform best practices for (1)-(7).
NIST manages a multi-faceted program, assisting communities and stakeholders on issues related
to buildings and the interdependencies of physical infrastructure systems. The Community
Resilience Program, part of NIST's broader disaster resilience work, complements efforts by othersin the public and private sectors. NIST focuses on research, community planning and guidance,and stakeholder engagement. In addition to improvements to standardized field study protocols,
three projects within the Community Resilience Program are directly supported by the field study
in Lumberton, NC.
Development of a First-Generation Community-Resilience Systems Model – The main
challenge confronting design or planning for community resilience is the need to address thelarge scale of the resilience system in question and the need to address social, political,
budgetary and other constraints. In the development of community plans (e.g., land use
13management, emergency response, economic development), the formidable challenges inherent
in both the analysis and design of the resilience system must be addressed. The analysis mustaccount for or include: a) stochastic phenomena (e.g., timing and severity of hazards, component
failure); b) dependencies between and among the resilience subsystems; c) time aspects ofresilience (e.g., preparedness through long-term recovery, aging infrastructure); d) connection of
the built environment to the social services it supports; e) significant uncertainties in knowledge;f) metrics to quantify community resilience, and; g) the large amount and diversity of dataneeded to characterize a community’s resilience system.
In this project a model is being developed to support community resilience decision-making.
This model will be accessible to planners and others with a need to identify alternative options
for resilience. The NIST Alternatives for Resilient Communities (NIST ARC) model and its
methods will first be tested in a research version of the model (NIST ARC-R). Data collectedduring field studies for past hazard events will be used to both inform and validate NIST ARC.
NIST ARC is being developed as a screening tool for identifying sets of alternatives that could
be analyzed in greater detail with IN-CORE. The results of the NIST ARC model are being
systematically compared with IN-CORE simulation models to ensure both models can provide
accurate results for their intended use.
To address these significant challenges, the research plan adopts an operations research (OR)approach to the problem of community resilience planning. OR is a quantitative approach that is
uniquely suited to large scale problems. A main tool of operations research, mathematicalprogramming, is applied here. Formulations (“math programs”) that describe community
resilience problems are developed and then are solved using available optimization solvers. Theinteractive formulation and solution of these models effectively provides decision-makers theability to identify cost-effective, resilience-improving alternatives that can be the basis of thesolutions ultimately adopted.
Development of a First-Generation Community Resilience Assessment Methodology –NIST released the Community Resilience Planning Guide for Buildings and Infrastructure
Systems to help communities plan and implement prioritized measures for the built environment
to strengthen their resilience to hazard events. The next step is to provide communities with thetools necessary to evaluate and measure their resilience over time. A more resilient community
will have, among many other characteristics, improved functionality of buildings andinfrastructure systems and a shorter recovery time of community functions following disruption.
This project is developing the methodology required to build a community-scale resilienceassessment tool. The methodology is based on a foundational understanding that community
functions are linked to buildings and infrastructure systems. Examples of community functions
are the following: housing/shelter, the economy, health, education, sustenance, public safety,
communication, transportation, religion/culture, and recreation/entertainment. Each function isdelivered through interconnected components of the social system (e.g., the banking system,
health care system, personnel/staff, consumers) and the physical system (e.g., building clusters,
transportation networks, communication networks). Both social and physical systems influencecommunity resilience – or a community’s ability to function after a disruptive hazard event.

14

The final methodology and accompanying Tracking Community Resilience (TraCR) tool will
include the following: selected priority indicators, the analytical approach(es) for computing
each indicator over time in a relevant manner for at least one spatial scale, best practices for how
the approach can be replicated for different spatial scales, public data sources for all indicators,
data visualization for the indicators, multivariate analyses to examine relationships between
indicators, sensitivity and uncertainty analysis, and validation studies.
Developing Cost-Effective Resource Allocation Strategies to Enhance Community
Resilience - Advancements in measurement science are needed to estimate the economic impact
associated with community resilience planning for natural and human-made hazards. Currently,
disaster-related loss (damage) estimates are available, although they tend to focus on direct loss
only and are at aggregated levels; these estimates often fail to consider down-stream, indirect,
and sustained effects, such as business interruption, which can be large and have a significant
effect on the short- and long-term stability of a local or regional economy. Also missing are
estimates of the cost of prevention, protection, and mitigation of hazards and potential disasterevents, as well as the expenditures required during the response and recovery phases. A
methodology to value the economic impact and avoided costs is needed to evaluate the returnon-investment of community resource allocation decisions made to reduce future economic
damages from disturbances and disasters, while recognizing the need to achieve balance withother community goals, and to account for uncertainty.
This project includes five major activities: (1) the creation and formal acceptance of draft
standard practices to formalize, publish, and make available an economic toolkit facilitating
decision making for communities; (2) measurement of disturbance and disaster-related costs andpotential linkages to achieve other community goals; (3) measurement of disaster losses,
focusing on major indirect losses, such as business interruption, and distributional effects—
through the use of both data gathered in the field through surveys and interviews as well as
secondary data sources; (4) quantification of the uncertainty affecting economic decisions, andunderstanding how uncertainty should be communicated to decision makers; and (5) measure the‘resilience dividend,’ the (non-disaster related) community co-benefits from investing in disaster
resilience, and provide tractable guidance to communities on approaches to assessing the net cobenefits associated with resilience planning.

1.3 Hurricane Matthew and the 2016 (Wave 1) Field Study Overview
In early October 2016, after already devastating parts of the Caribbean, Hurricane Matthew
struck Florida, and continued up the eastern seaboard before turning out into the Atlantic Ocean
off the coast of North Carolina and Virginia (see Figure 1-1). More than 170 counties in Florida,
Georgia, South Carolina, and North Carolina were included in Presidential Emergency
Declarations and/or Presidential Disaster Declarations between October 6th and 11th, 2016.
Economic loss estimates exceed $10 billion (NOAA 2018).
More than a week after the storm turned out to sea, parts of North Carolina had yet to experience
flood crests, with many communities experiencing flood levels at or higher than those for15

Hurricane Floyd in 19992. The Lumber River reached flood stage in Lumberton, North Carolina
on October 3rd due to local heavy rains. On October 11th, the Lumber River crested at almost 22
ft (6.7 m) above the gage datum. The water level slowly fell, dropping below flood level on
October 23, 2016.

Figure 1-1. Path and Intensity Timeline of Hurricane Matthew.
As indicated in van de Lindt, Peacock, Mitrani-Reiser et al. (2018), the Center Field Study teamselected Lumberton for longitudinal study for many reasons, including the moderate population
size of approximately 21,000 residents (US Census Bureau 2017), the diverse socio-demographic
makeup of primarily three race and ethnicity groups (White, Black, and Lumbee Indian), and that2

At its peak, Hurricane Floyd was recorded as a Category 4 hurricane. It became a Category 2 by early September
1999 when it impacted North Carolina with a storm surge height exceeding 9 feet causing 51 fatalities and billions
in damages. Flooding damage was tremendous with as much as 20 feet of floodwater staying for over a week insome areas and exacerbated due to Hurricane Dennis which hit North Carolina just a few weeks prior.

16

flood waters entered the City through a gap in the levee system that 13 years prior was reported
to not meet the current FEMA regulations. The impact of the hazard event on the community,
including public school closures in the county, was also a significant factor in the decision.
The NIST-funded Center for Risk-Based Community Resilience Planning teamed withresearchers from NIST’s Engineering Laboratory, specifically the Community Resilience,
Disaster Failure Studies, and Applied Economics programs, to conduct a quick response fieldstudy focused on the small city of Lumberton, NC and the flooding Lumberton experienced fromthe Lumber River. The quick response field study was performed during the week of November
29, 2016. Denoted here as Wave 1, it was the first of a series of annual field studies to document
and better understand Lumberton’s recovery. Data collection during Wave 1 focused on theresidential housing sector with two primary objectives, including: to establish and document
initial conditions for the longitudinal resilience field study of Lumberton’s recovery, with a focus
on the most heavily affected area located within a particular school zone; and secondly, tofacilitate and document the development and first application of a combined engineering-social
science field study protocol that provides a quantitative linkage between flood damage andsocio-economics including race, ethnicity, income, tenancy status, and education level. The study
concluded that flood inundation levels and subsequent flood damage were not evenly distributed
for the people of Lumberton, but rather trends of intersecting physical and social vulnerability
were observed. Population dislocation probabilities were found to be higher for Black and Native
American households than for White households, given the presence of the same residential
housing damage state following the flood. See van de Lindt, Peacock, Mitrani-Reiser, et al.
(2018) for the Wave 1 field investigation report.

1.4 Overview of the 2018 (Wave 2) Lumberton Field Study
Approximately one year after Hurricane Matthew, during the dates of January 19 to 29, 2018, theCenter and NIST researchers returned to Lumberton for Wave 2 of the longitudinal study. The
overall purpose of the longitudinal field study was to (1) support on-going research in the Centerand NIST through the collection of the necessary data to build and/or validate communityresilience models for business, housing, social institutions, and building functionality; and (2)advance understanding on the factors that influence recovery for two specific community sectors,
namely housing and business, as well as to gain information on the recovery status of schools,
households, public works, and the community as a whole. While Wave 1 documented the initialphysical and socio-economic impacts of the flooding on the community, particularly for housing,households, schools, and other parts of the public sector, Wave 2 began the assessment ofrecovery. A variety of recovery indicators were employed in Wave 2 to support the measurementof Lumberton’s progress in recovery. These metrics will be used in Waves 3 and beyond to
document the trajectory for housing and businesses, as well as provide multi-sector insight to thecommunity’s recovery trajectory.
In support of the overall purpose of the longitudinal study, the housing sample of 567 housing
units from Wave 1 was maintained in Wave 2 with a continued focus on the housing unit.3 Of thetotal of 567 housing units in the sample, the team surveyed 221 households with additional3

For housing, one housing unit was dropped from the sample due to a hard refusal from the residents in Wave 1,
resulting in a sample of 567 housing units for surveys in Wave 2.

17

responses provided by a neighbor for a response rate of 50 %. In Wave 2, a new component of
the data collection was added to study business interruption and recovery. Similar to returning
people to their homes, restoring the local economy following a disaster is one of the mostimportant collective goals for a community. The Lumberton business community has a mixtureof franchise-based and locally-owned businesses across many sectors. Knowing how thesebusinesses were impacted, if they closed and why, and if and when they re-opened was critical tounderstanding Hurricane Matthew and the flood’s impact on the community. During theLumberton Wave 1 field study it was clear that many businesses were damaged or otherwise
disrupted; the research team determined that there is a need in resilience modeling to better
understand the impact of long household dislocation durations on business interruption andrecovery and vice versa, i.e. interdependency. Although Wave 1 data collection did not include
engineering damage assessments for this sample of businesses, the Wave 2 business survey
instrument assessed the damage from Hurricane Matthew through a series of questions built on
damage state descriptions specific to commercial buildings. For the business interruption andrecovery study, a sample consisting of 453 businesses was added for Wave 2.4 Of the total of 453
businesses, the team surveyed 164 business owners and managers for an overall response rate of
36 %.
Both the housing and business surveys included questions on physical repair and sector-specific
recovery indicators to document recovery progress and asked about the availability and timing ofa range of financial recovery resources. For the public sector data collection, meetings were heldwith four city representatives and four state representatives, including both government and thewater utility, to understand the context for recovery of the community. Likewise, to understandthe context for school recovery, interviews were conducted with nine school district
representatives.

1.5 The Scope and Audience for this Report
The purpose of this second community resilience field study is to provide measurement ofrecovery of post-disaster conditions, both physical and non-physical, for one community. To
achieve this, more than 200 households and more than 150 businesses were surveyed, andseveral interviews and meetings were conducted with local, state and federal officials during
January 2018 by the field study team. This report summarizes the results of the data collected byan interdisciplinary team of engineers, social scientists, and economists. The instruments
developed for Wave 2 along with the data collected will be used in Waves 3 and beyond to track
and present the recovery progress for Lumberton. This field study is intended to provide datarequired to calibrate models within IN-CORE and other community resilience modeling tools.
The data will ultimately be used to help validate the complex and coupled physical and nonphysical modeling processes being developed within the Center of Excellence. It is envisionedthat this report can also provide objective information on the impacts, response, and recovery
processes as documented by an outside research team to the relevant local, state, and federal
officials. These purposes provide a mechanism by which to learn from the events in Lumberton
4

For businesses, a random stratified sampling approach resulted in a sample of 350 businesses drawn from theReferenceUSA database (InfoGroup 2016). While in the field, an additional sample of 103 businesses was drawn toaddress possible coding errors in ReferenceUSA, business closures, and a low response rate. The final sample
resulted in 453 businesses.

18and to identify mechanisms to help other communities plan, prepare for, and recover fromnatural hazards such as floods. Therefore, this report is aimed at an audience of researchers andpractitioners interested in resilience, including those in academia, governmental labs, industry,
local and state planning, and community officials working toward improved resilience.
Overall, there were a number of key takeaways from Wave 2, including that recovery was still an
on-going process for Lumberton 14 months after flood water receded. The majority ofhouseholds did not report their dislocation time being influenced by changes to work, school, orother businesses; their primary decision factors to reoccupy their home were physical repair of
their home and utility service restoration. A quarter of the housing sample was documented as
being abandoned; the reasons these households had not returned remain unknown but could be
due to the factors listed above. A very low percentage of at-risk households carried flood
insurance and the small percentage that applied for external assistance must be able to wait asubstantial period of time to receive the resources. Wave 2 results also documented clear
differences in how businesses were recovering including their perception of future recovery witha quarter operating below capacity to approximately a third still being in survival mode; almost
half of businesses reported a decrease in revenue since the flood. Federal relief funds were
found to be notably slower than other state and private funds which was apparent fromdiscussions at both the state and local government levels. Finally, schools within Robeson
County continue to struggle as students leave the district resulting in funding decreases andofficials indicate that repair funds are scarce.
Wave 3, and all subsequent data collection waves, will continue to survey the same samples of
housing units and businesses to continue documenting recovery trajectories for these sections, aswell as for Lumberton as a whole. The longitudinal comparison of the recovery metrics reported
above will, especially at such a fine resolution of housing unit- and business-level scales,
contextualized with community sector interviews, provide a rich and holistic depiction ofcommunity recovery that has never been documented before. The Wave 3 report, as well as
future reports, will also include documenting intervening events that might impact recovery from
Hurricane Matthew.
This report is organized as follows: three chapters focused on distinct samples, data collection
instruments, and approaches with a final chapter presenting conclusions. In Chapter 2, the studyof housing disruption and recovery are presented, including specific study goals, team training
and deployment, and descriptive results. In Chapter 3, the study of business interruption andrecovery are introduced, with an emphasis on the business survey methodology and descriptive
results. Chapter 4 details the study of public-sector impacts and the recovery process, which
utilized semi- and un-structured stakeholder interviews and meetings, including at the city- andstate-levels, as well as semi-structured interviews with school officials and staff. Finally, in
Chapter 5, conclusions on the findings of the previous chapters are provided along with next
steps for the longitudinal field study.

19

Chapter 2: Housing Disruption and Recovery
2.1 Goals and Objectives
The housing component of Wave 2 supports the on-going research in the Center, including
modeling housing damage, household dislocation, household relocation, and housing repair andrecovery in place. Specific goals across these efforts are summarized and then discussed ingreater detail below. The primary goal of the housing data collection for Wave 2 was to continue
the longitudinal study with data collection one year out from the initial data collection with afocus on the measurement of recovery indicators for housing and households. Corresponding
with changes in the disaster timeline for Lumberton households, the objectives of the housing
data collection for Wave 2 included:
1. Filling in missing information on initial housing damage not captured during Wave 1;
2. Improving documentation on households’ repair timelines for their homes considering
different initial damage levels, income levels, available recovery resources, and sociodemographic characteristics;
3. Developing a better understanding of what interdependent infrastructures and services
were considered in the households’ timing and decision to return to their home;
4. Obtaining household-level data on a range of factors that are hypothesized to influence
housing recovery, including incoming financial resources.
Housing Damage. Physical damage to housing is a primary cause for household dislocation in
the aftermath of a disaster. The primary goal of Wave 1 data collection was to validate a
proposed set of flood-induced damage classifications, and capture information on initial damage,utility outage, and dislocation for the housing sample. Wave 1 documentation on the initial
impact to the housing sample provided the necessary context for Wave 2. During Wave 1,
detailed damage evaluations were performed successfully for a large portion of the housing
sample using an engineering methodology. Given the widespread and long-lasting dislocation ofmany households in Lumberton, interior damage was assessed for only a limited number ofsample units which could lead to over- or underestimating true damage. Due to the importance ofknowing initial damage to every sample, Wave 2 re-assessed damage using social science
methodology to ensure this information was collected for all sample units for which recovery
information was obtained. The Wave 2 damage classification approach differed accordingly
since initial physical damage was no longer apparent. The Wave 2 damage questions sought
information from the households on the level of damage their home experienced using the samedamage classification descriptions as Wave 1, enabling the field study team to fill in, confirm, or
expand the initial damage evaluations on the sample units.
Household Dislocation. The dislocation of the population is both an immediate, and, in somecases, a lasting impact of a disaster. The Wave 1 household survey (Peacock et al. 2020) focusedon the social and economic impacts of the flooding, including dislocation. The Wave 2 survey
has the potential to improve household dislocation models in two ways. First, Wave 2 addressed
documenting dependencies. This was achieved by collecting additional data on dislocation andfactors shaping dislocation, such as household socio-demographics, tenancy status, insurance andother financial recovery resources, and access to work, school, and retail establishments.
Secondly, Wave 2 extended the dislocation period captured in the surveys from up to 1 month in20

Wave 1 to 15 months. The data collected in Wave 2 therefore enables the continued analysis of
dislocation time and factors influencing when and why dislocated households return home.
Household Relocation. Wave 2 leveraged the longitudinal study design by revisiting the same
housing sample to document when a former household moved following Hurricane Matthew.
This information is being used to investigate the relationship between household relocation,
initial damage level of the unit, dislocation time, and access to social, economic, and public
institutions, including work, school, and essential needs. This work fits into the larger Center
research which examines how permanent residential relocation after disasters has led to thechange of demographic characteristics of impacted areas. Therefore, in addition to thehousehold-level assessment, future Center research will include a community-level assessmentthat will compare the aggregate socio-demographic data across field study waves to investigate
whether there is a relationship between the housing relocation rate and neighborhood
demographic change. This analysis will inform an understanding of how housing and household
recovery fit into community recovery.
Housing Repair and Recovery In-Place. Similar to housing damage, the speed of repair is a
primary factor in explaining why households return or relocate after a disaster. Limited
information is available in the literature about the time and overall process for repairing damaged
structures. As pointed out by Mitrani-Reiser (2007), the repair process is complex and includes
much more than reconstruction; it also includes the time to understand and evaluate damage, hirea contractor, get materials on site, develop a design, obtain a permit, and reconstruct or repair the
structure. The time to repair is lengthened when considering that the portfolio of damagedbuildings and other infrastructure in the community all need repair at the same time with limited
resources available. Furthermore, homeowners or landlords can spend a considerable amount oftime deciding whether they want to rebuild or relocate or neither rebuild nor relocate a rental
home (Nejat and Damnjanovic 2012). The Wave 2 survey documented how long it tookhouseholds to repair their homes considering different initial damage levels, income levels,
available recovery resources, and socio-economic and demographic characteristics. Additionally,
the influence of interdependent infrastructures, including electric power and water, andcommunity-level social and economic institutions on household recovery in-place was examined.
Housing recovery is an unequal and complex dynamic process with many dependencies andinterdependencies (Sutley and Hamideh 2017). Access to financial resources, including personalsavings, insurance, federal assistance, and non-government organization (NGO) funds, is a
primary contributor to how households experience housing recovery. Quantifying the effect of ahousehold’s ability to access financial resources on housing recovery has not yet been achieved.
This lack of empirical modeling and/or validation is the result of a dearth of data to support suchinvestigations. Therefore, Wave 2 obtained household-level data on a range of factors expected
to influence housing recovery in-place including financial resources, amount of damage, sociodemographic characteristics, tenure status, repair time, decision making, and access to
infrastructure and essential community functions.
The research-to-practice intentions of the housing component of Wave 2 data collection include(1) measuring the unique contribution each physical, social, demographic, and economic factor(while controlling for the other factors) in a household’s ability to reach a state of permanent21

housing; and (2) using the housing recovery measurement to inform on-going research andmodeling efforts, including identifying policy levers (e.g., policies that speed up or change the
distribution of financial and other recovery resources) in decision algorithms that could reduce
housing recovery inequities (Sutley and Hamideh 2020).

2.2 Sampling Procedure
To continue the longitudinal study, Wave 2 utilized the same housing sample as Wave 1. The
study area for Lumberton was defined by the school attendance zone for Lumberton Junior High,
which includes the attendance zones for the two elementary schools (W.H. Knuckles andTanglewood Elementary Schools). A two-stage non-proportional cluster sample was designed tocapture a representative random sample of the study area, which included areas inundated by
2016 Hurricane Matthew flooding as well as areas not directly impacted by the flooding. Within
this design the penultimate sampling units were census blocks, and primary sampling units were
housing units and households residing in those units. The penultimate sampling units (blocks)
were selected utilizing a probability proportion to size (PPS) random sampling procedure, withblocks in areas with a high probability of flooding selected 3-to-1 over areas with low flooding
probability. Areas with a high probability of flooding were identified as those inside the team’s
predicted flood inundation area from aerial imagery. Additionally, a 100-m buffer was added to
the floodplain areas to account for uncertainty. Housing units within the sampled blocks werethen selected on a fixed rate of 10 random units per block, with 8 units identified as primary with
2 alternates. The combination of selection PPS with a fixed number of housing units selected,
after weighting, assures a representative sample of the area. Among the 830 blocks with 5 ormore occupied housing units in the school attendance zone, the sampling process drew a random
sample of 80 blocks based on a probability of selection proportionate to size, oversampling forhigh probability flooding (56 census blocks in the high probability areas and 24 in the low
probability areas). In the final analysis, 75 of 80 census blocks were visited in the final sample(54 census blocks in the high probability areas and 21 in the low probability areas), including
568 valid primary housing units5, yielding an average of 7.6 housing units per block. For moredetailed information about the sampling procedures for Wave 1, please refer to section 3.2.1 inthe Wave 1 report (van de Lindt, Peacock, Mitrani-Reiser, et al. 2018).
The 568 housing units, and the households living in these units, were the primary sample units
for Wave 1 damage assessment and household data collection and the target sample units for the
Wave 2 household survey. During Wave 1, 13 of the 568 selected households were refusals ofsome nature, two were households new to original housing units, one did not have an adultavailable to answer the survey, and 259 were either not occupied or presented some otherproblem for undertaking an interview. Among the 13 refusals, one household showed a very
explicit rejection and accordingly, this housing unit was dropped entirely from the Wave 2
housing sample, while “soft refusals” and temporarily unoccupied houses were re-visited.
Therefore, Wave 2 consisted of a selected sample of 567 housing units. Figure 2-1 shows thelocations of these 567 housing units alongside flood and school zone information.

5

Invalid housing units included housing units where a gate or other barrier prevented access and where response
was documented as a “hard refusal”.

22Figure 2-1. Sampled Housing Units (1 mi = 1.6 km).

2.3 Survey Instrument
Multiple researchers within the Center and NIST, each with unique expertise and interest in
disaster recovery, participated in developing the survey. Having a multidisciplinary team helped
ensure inclusion of different dimensions of household and community recovery in conjunction
with housing recovery. Pretesting and training were two integral steps in the survey development
process. During development, the survey instrument underwent several rounds of review andthrough a pre-testing and training process.
The final housing survey instrument also went through review for the Paperwork Reduction Act(PRA) (1995. Pub. L. No. 104-13, 109 Stat 163). The purpose of this review is to: “ensure thegreatest possible public benefit from and maximize the utility of information created, collected,
maintained, used, shared, and disseminated by or for the Federal Government; and to “improve
the quality and use of Federal information to strengthen decision making, accountability, andopenness in Government and society.” The instrument and data collection methodology for thehousehold survey and the full Wave 2 Lumberton study were approved by the Institutional
Review Board (IRB) at Colorado State University, which oversees the Center’s human subjects
research.
The Wave 2 survey built upon several questions from the Wave 1 survey on household
dislocation, socio-demographics, and occupancy status to increase the reliability of thelongitudinal data. New components of the survey drew from team expertise and existing survey
instruments on housing recovery and disaster impacts on children and families (Abramson et al.
2010), and targeted specific gaps in the literature such as the role of different financial resources23

in housing recovery outcomes that were critical for inclusion in IN-CORE housing recovery
models.
In general, there are seven main sections of the survey, including establishing occupancy andstudy eligibility, housing damage, housing repair and recovery in place, household dislocation,
household relocation decision, educational recovery, and household socio-demographic
characteristics. See Appendix 2A for the Wave 2 housing survey.
Establishing Occupancy and Study Eligibility. Upon approaching a housing sample, surveyors
observed the landscape for safety6, accessibility, and occupancy. There were two places to record
information based on these initial observations and based on the result of the survey. The first ofthese was titled ‘Result / Completion Codes’, and included options for survey completion;
incomplete or partial completion; no answer or response but appears occupied; ineligibility
because no eligible person was available; ineligible because the structure is not a residence; badaddress; no access; or not occupied and appears abandoned or destroyed. Refusals were recorded
as “no response but appears occupied” and a note was written in the comments to document therefusal.
The second item was titled ‘Housing Unit Occupancy Status’ and included options similar to theprevious but wholly focused on occupancy, rather than survey completion. If the household was
not present but their neighbor or landlord was outside, or their neighbor was residing in a
housing unit that was part of the sample, then the neighbor or manager was asked five questions
related to occupancy and dislocation caused by the flooding.
If the household was present and provided oral consent to being surveyed, then the first question
was to determine whether the household had been living in that housing unit since before
Hurricane Matthew. If a household had moved in since Hurricane Matthew, denoted as ‘newhousehold’ throughout, they were asked 10 questions. If the household had lived in the housing
unit since before Hurricane Matthew, denoted as ‘continuous household’, they were asked 25
questions. The housing survey was administered via single mode of a surveyor reading thequestions aloud to the respondent. The scope of these questions is as follows:
Housing Damage. Damage questions were intended to capture the extent of damage from
Hurricane Matthew from the perception of the household and provide a point of comparison withthe expert damage assessments conducted during Wave 1. It is important to note, the occupant
may exaggerate or underestimate damage, or not remember specific water heights, so only
obtaining damage information from this single source may introduce measurement bias.
Conversely, people are generally able to remember exactly how much repair work was required
which captures information beyond a damage evaluation driven by an external watermark, andobtains information on what had to be gutted, repaired, and replaced. In cases of low damage,
water marks may not be visible. In Lumberton, where the flood water remained stagnant for
many days and residents were dislocated for long periods of time (leading to a slow start torepairs), the initial damage observed in Wave 1 may not have been the peak damage for some
6

Safety is a consideration in any field deployment and is an important part of any field training. See Appendices5B-5C.

24housing units. The follow up in Wave 2 enabled a comparison of methods and results whenpossible, or to otherwise supplement missing information from Wave 1.
Housing Repair and Recovery in Place. The repair and recovery in place portion of the survey
was designed to document how long it took households to repair their homes, and what financial
resources they had available for that purpose. The survey did not capture when repairs were
initiated.
Questions on housing recovery resources posed during Wave 2 were one of the first attempts tocapture household level data on a comprehensive range of resources from insurance, government
assistance programs, and households’ social networks (Sutley and Hamideh 2017). The survey
captures information about the timing of application and receipt of each type of resource, as wellas adequacy of all combined resources received. Four indicators of housing recovery wereincorporated into the survey, including repair completion, re-occupancy, restored stability, andrestored accessibility, where stability means the household intends to stay in that home for atleast one year, and accessibility means the household has at least the same access to essential
needs in the community as they did before the event.
Household Dislocation. Longitudinal data enables the modeling of household initialdisplacement, whether there was a return to the pre-disaster home, the timing of return, as well as
permanent relocation and the arrival of new households who move into housing units after thedisaster, while limiting the introduction of measurement bias that may occur when only asking
about these items once recovery is complete. Of particular importance is gathering informationon the length of time for household return. Household dislocation questions covered the lengthand reasons for the dislocation, such as whether or not jobs, restoration of infrastructure, ordifferent community functions such as schools and shopping facilities affected the household
length of dislocation.
Household Relocation Decision. Relocation questions were designed for all households in thedata collection effort. Upon approaching a housing unit, when no one answers the door, aneighbor or potential apartment manager is sought. During these engagements, the survey asks
whether the residents lived in the housing unit at the time of Hurricane Matthew, and if not
currently living in the unit, where they had permanently relocated to (staying in the city, within
the State, or out of State). Similarly, when someone did answer the door to a housing unit, one of
the first survey questions asks households when they moved into the present home, and whether
there was Hurricane Matthew-induced damage to the home. If the household moved in after
Hurricane Matthew, the survey asks questions about occupancy before Hurricane Matthew andgeneral whereabouts of the former household. Finally, when a household member answers thedoor and indicates that he/she is a resident of the same home before Hurricane Matthew, thesurvey asks about future relocation decisions to examine whether households have reachedpermanent housing recovery in the current location. This is followed by a group of questions that
document whether access to different infrastructure and community functions might affect thehousehold’s decision to stay or leave that housing unit (i.e., stability).
Educational Recovery. The survey made distinctions between children and adult members ofhouseholds in order to collect data on the links between housing and school recovery.
25

Households with school-aged children were asked about their children’s progress towards
educational recovery relative to their situation before the flood disaster. These questions were
motivated by the temporary and permanent school closures in Lumberton after Hurricane
Matthew.
Household Socio-demographics Characteristics. Most questions on household characteristics
were repeated from the Wave 1 survey, and included categorizing respondents as renters orowners, and obtaining information to categorize the highest education level of any member in thehousehold, racial makeup, ethnicity, and combined household annual income. These types ofquestions serve to document whether the sample matched the distribution of socio-demographics
indicated in census data. Demographics also support examining dislocation, relocation, repair,and recovery processes that may differ due to household socio-demographics, as is expected due
to social vulnerability theory (Fothergill and Peek 2004).

2.4 Data Collection Methodology
The surveys were conducted face-to-face by teams of two. A consent script was used to obtain
verbal consent prior to surveying (see Appendix 2C for the consent script). The surveying took
place across both weekends of the field visit from January 19 to 29, 2018, as well as during the
weekdays with a more limited team. In addition to required IRB human subjects ethics training,
all surveyors received team training on ethics, survey use, planned field coordination, and bestpractices for in-field safety prior to being in the field, and additional guidance and training whenin the field (see Appendix 5B for field study safety protocols). This section describes daily
operations and data management.
2.4.1 Daily Operations
Daily Process. Each morning, the full Wave 2 data collection team convened to review logistics
for the coming day, including arranging teams, vehicles, drivers, creating a check-in schedule,
distributing packets of data collection materials (e.g., surveys, photo release forms, supplemental
information for respondents, interview guides, personal protective gear; see Appendix for
details), and preparing for the day’s data collection. As needed, training sessions would be folded
into this schedule to accommodate new team member arrivals or shifts in team composition.
Researchers responsible for the public sector interviews (see Chapter 4) checked in with the fullteam at lunch or at another designated time during the day. Survey preparation included reviewof the survey instrument, review of the sample cluster, preparation of packets for each sample
cluster, and discussion of issues with survey questions, answer options, data recording in thefield, or data entry.
Each evening, the full field study team met to enter and back up data, including audio recordings,
photos, video, and laser scan imagery, update the sample status based on housing units visited,
conduct any necessary correspondence to confirm upcoming interviews, and review any issues
encountered from the day. At the end of each day, new data was entered into Qualtrics survey
software by the survey team. The decision to adopt a protocol that included daily data entry was
aimed at increasing the accuracy of the data entry and easing the time required for the data
cleaning process after the field work concluded. Similarly, the sample database was updated each
evening with information pertaining to the visits from that day as mentioned above –26

specifically, daily updates to the sample map, generated through Google MyMaps3, guided theteam’s survey work (similar to what was done in Wave 1; see Appendix 5A for more details,
including a supporting figure).
Although resources (e.g., staff, time, and funds) were limiting factors, several actions were taken
to improve the outcomes of the field study and data collection. To ensure a higher response rate
to the housing survey, the team:● Trained surveyors for maximum efficiency in the field;● Concentrated surveying on weekends and evenings;● Made repeat visits to housing units;● Arranged scheduled follow up times for households not available for surveying during theinitial visit (if willing to participate);● And, adjusted the field work plan and team composition based upon daily evaluation of
results.
Driving Assessments. Prior to surveying in a new cluster, and as time permitted, the teams
performed driving assessments of the clusters to identify housing units that were clearly
unoccupied or abandoned. This assessment allowed for increased survey efficiency on the
ground, as time was better concentrated on the housing units for which a response was likely.
The process was as follows: teams of at least 2, ideally 3, individuals would travel in a single
vehicle with one person driving, one navigating a route through the cluster, and a third (or thenavigator) documenting the housing unit ID of each unoccupied or abandoned housing unit andcapturing a photo of the housing unit.
Times of Day. Emphasis was placed on concentrating surveyor capacity on days and times when
response rates would be highest. Weekend days, afternoons, and evenings were the focus of theeffort, although there were teams that covered weekdays throughout the field study period. Early
mornings (before 9am), and Sunday mornings (before noon) were avoided to respect cultural
norms of the area. Teams completed their data collection before dusk.
Team Composition. Representation of multiple disciplines was a primary focus of Wave 1 teamcomposition. In Wave 2, the focus was on experience with surveying, knowledge of the area, andgender. With the exception of survey experience, the other factors were driven by an awarenessof the importance of safety and matching best practices for field research in any location. Teamsof four would travel in a single vehicle and work through one cluster together. Pairs of surveyors
worked opposite sides of a street so that they were almost always within view of one another. A
vehicle would be moved when the team would complete a small geographic area (e.g., half of a
block).
2.4.2 Data Management
Following the field work, all physical data was stored in a locked file cabinet and all electronic
media was saved on password protected computers of the principal investigators that were kept
in locked offices. A linked-list was created where all identifiable information was replaced withcode numbers. The same codes were used for the field notes and photographs from each site. No
names were attached to this documentation. Original data access was limited to project
investigators from the Center and NIST who have completed the IRB training and whose
27

universities signed the Interagency Agreement (IAA). The data will be maintained by the Centerfor the three-year archive period following the conclusion of the study and will then be archived
with NIST.
Documenting survey responses. Data collection was completed using paper-based survey
forms, which were read aloud and responses were recorded by the surveyors for respondents.
Data collectors carried all survey materials on clipboards including answer cards for select items(e.g., damage states and income levels, see Appendix 2B), resources for respondents (e.g.,
mental health services and housing recovery information, see Appendix 1B), and project
information sheets (see Appendix 1A). The survey forms included the housing unit ID as theprimary identifier; surveyors recorded the ID on each page of the form in the event that pages
were separated. Before beginning the next survey, survey teams would capture any additional
notes and clean up data entered on the survey form. Surveyor guidance was included in thesurvey form to support the proper elicitation and recording of responses when in the field.
Photographs. Photographs of the housing units taken during Wave 1 were used to notedifferences over time. In general, less damage and debris were visible during the Wave 2 fieldcollection. As a result, fewer photographs were taken as part of Wave 2 data collection.
Exceptions include capturing the unoccupied housing units, many of which had not experienced
any repair or reconstruction; field team members photographed any housing unit that appeared to
be unoccupied as additional evidence of its status. Additionally, as was done in Wave 1, Wave 2
vehicles drove around Lumberton with ‘streetview’ style 360-degree cameras to capture imageryof the community’s physical recovery. Streetview imagery is being compared across waves todocument changes in debris cleanup and physical repair and reconstruction and to supplement
photographs by survey teams and to document recovery progress.
Daily procedures of entry and back up. A number of steps were completed each day to ensurethat all data were managed appropriately and in accordance with the IRB protocol [15-6003H].
Each evening upon return from the field, the team would complete the data entry of all survey
forms recorded on that day, which included complete surveys as well as incomplete surveys. The
information was entered either by a member that directly recorded the data, or by a member thatwas working in the same cluster and traveled in the same vehicle. The data were entered using
Qualtrics and stored on a cloud server. All hardcopies of the survey collection forms were thenstored in locked cabinets by the field study leads.
As with the surveys, all photographs taken while surveying or during any of the driving
assessments were downloaded and backed up to a server. This back up procedure was the samefor photos, video taken with the vehicle mounted 360-degree camera, and LiDAR scans. NIST
researchers also backed up all photo and video data on an external hard drive and immediatelytransferred data to secure file storage locations at NIST following the field study.

2.5 Survey Results
The data for housing recovery were analyzed independently, though they will later contribute toa broader suite of analyses as part of the longitudinal study for Lumberton, NC. This section
presents the housing survey summary statistics resulting from Wave 2. Detailed, comprehensive
analyses are being performed and presented outside of this report.
28

2.5.1 Data Cleaning
As discussed in Section 2.4, at the end of each day the new data was entered into Qualtrics by
the survey team. As with all data collection, the data required some cleaning over a period offour months. This included double-checking each entry for obvious typographical errors with theoriginal hard copy of the survey and updating or newly entering data to correspond to any
changes in the data entry form that were not available at the time of original data entry (e.g.,
items where programming was adjusted for improved consistency of data entry while in the fieldrequired cleaning after the field visit). Changes and decisions adopted during data cleaning aredetailed below.
There were some issues collecting accurate and consistent data on dislocation and recovery
timing. In addition, memory bias of the interviewees could not be avoided. For example, some of
the temporal data was specified to be in a calendar date format, while other temporal data was
specified to be a number of days, weeks, or months after the disaster. This latter type of question
risks error from not knowing exactly when the disaster “starts” from each household’s
perspective and count. For example, some households dislocated before the flood waters rose tohigh levels; some households were out of town for unrelated purposes; other households were
rescued via boat once the flood waters got too high for them to safely leave on their own. As was
discovered through implementing the surveys, not every household remembered the exact day
the flood waters reached high levels, or what calendar date they left town. Most households did
exhibit confidence in identifying a number of days (for short durations), weeks (for moderate
durations), and months (for long durations) that they were dislocated or were waiting on theirhome to be fully repaired. Temporal data recorded on the survey or in Qualtrics as “1 week” wasconverted to equate to “7 days”. Similarly, temporal data recorded as “1 month” was converted
to equate to “30 days” (obviously not all months are 30 days, but this approach provided an
appropriate balance of computational time to measurement certainty). When a date was provided(DD/MM/YY), October 10, 2016 was used as the disaster start date to be consistent with the2016 field study.
Other issues for data cleaning included new categories for “refusal” to be a documented outcomeof a survey attempt and new occupancy options. When refusals were received, they wererecorded into the comments section. A new option for refusal as the survey completion code wasadded into Qualtrics and to the data while cleaning. Similarly, there were houses that appeared to
be in good condition but also unoccupied, sometimes with a ‘for sale’ sign present. The printed
survey did not have an option to mark “unoccupied, but not abandoned,” so again interviewers
recorded this in the comments section. This occupancy option was added to Qualtrics and to thedata while cleaning. In one instance, the data collection team learned from neighbors that theresident had passed away either before or after the flooding. The printed survey did not have an
option for a resident that will not return because they are deceased, but notes were made by thesurveyor in the comments section. This occupancy and relocation option was added to Qualtrics
and to the data while cleaning. Lastly, although the surveyor training required that answers mustbe specified per the respondent’s best estimate, sometimes a range was recorded for the timing of
events or for the income category. In all cases where a range was given, the higher value wastaken (e.g., 2 to 3 months = 3 months).

29

2.5.2 Survey Response Rates
Data was collected from three types of interviewees: continuous residents, new residents, andwhen the resident was not available, a neighbor or manager was sought for limited information.
As discussed in Section 2.3, new residents are those who moved into the housing unit after
Hurricane Matthew and the subsequent flooding. Continuous residents are those who lived in the
housing unit at the time of Hurricane Matthew and the flooding. Continuous residents were readthe longest set of questions on the survey, as stated in Section 2.3.
Survey completion rates are reported in Table 2-1 as ineligible, abandoned, no answer, refusal,
and completed surveys. Ineligibility could have occurred due to having no access to the property(e.g., gated fence, dangerous crossing), or when a non-residential property appeared in thesample due to original miscoding or property changes since the flood. Abandoned sample units
were marked when there was an existing residential unit on the property with evidence ofabandonment. This evidence was based on either the surveyor’s opinion or gained throughconsultation with a neighbor. No answer was marked when the residential unit was visited, and(1) it did not appear to be abandoned, but there was no answer at the door by the resident and no
consultation with a neighbor or manager, or (2) a child answered the door and there was no adultavailable. Refusals include those households who answered the door but declined to speak withthe survey team through either soft or hard refusals.7 Completed surveys are reported for thosesample units where surveyors were able to complete at least a portion of the survey with them.
The rate of completed surveys is provided for the three types of interviewees. When subtracting
the ineligible sample units from our original household sample (n = 555), and counting
abandoned sample units as responses, the overall response rate was 77 %. Without considering
abandoned sample units as responses, the response rate is 50 %. When only considering
completions by new and continuous residents, the response rate was 40 %.
Table 2-1. Housing Survey Completion Rates.
Housing
Type

SampleIneligible

AbandonedNo
AnswerRefusalCompletion
by
Neighbor

Completion
by New
Resident

Completion
byContinuous
Resident

%

Tot.

%

No.

%

No.

%

No.

%

No.

%

No.

%

No.

%

No.

76

431

75

9

64

96

78

76

77

23

85

47

64

28

86

152

11

60

0

0

11

17

14

14

17

5

5

3

20

9

7

12

Duplex

11

63

0

0

23

34

6

6

7

2

5

3

16

7

6

11

Mobile
Home
Total

2

13
567

25

3
12

3

4
151

2

2
98

0

0
30

4

2
55

0

0
44

1

2
177

SingleFamily
MultiFamily

7

A hard refusal is the term used to capture responses such as a door slammed in face, a demand
to get off the porch or the property, and being told not to return. A soft refusal is a response more
likely to be converted into a yes with additional explanation (e.g., importance of the response) or
accommodation (e.g., arranging to come back at a more convenient time).
30

Table 2-1 presents survey completion rates for the 567 housing units in the sample, with 420 in
the inundation area and 147 units outside the inundation area. Recall from Section 2.2, thesampling was proportioned at a rate of 1:3, such that for every selection of one housing unit witha low probability of flooding, the sample included three with a high probability of flooding.
When the sample weights are applied, the responses for low flooding probability sample units
are multiplied, such that there are three instances of each of these sample units in the data. Withweights, the total number of sample units increases from 567 to 861. This weighting is necessary
to draw accurate conclusions about the distribution of impact and recovery progress across thecommunity. The results that follow use the weighted sample size of 861 housing units.
2.5.3 Findings: Flood Impacts and Recovery
This section includes the descriptive statistics collected and re-proportioned on damage toresidential buildings, dislocation time for households, as well as other impacts and dislocationbased decision making, housing recovery progress, respondents’ perceived educational recovery
status of the children in their household, distribution of recovery resources and unmet needs forhouseholds, and demographics of those who completed surveys. Different numbers of
respondents responded to individual questions, but all reported numbers and percentages are outof the re-proportioned sample of 861, unless otherwise stated.
DamageTable 2-2 provides the resident-reported damage level from all residents who completed thesurvey or a portion of it (n = 310 or 36 %); 149 units were marked as abandoned. Of the 310
housing units who provided information on the initial damage level to their home, 100 (32 %)were in the inundation area. Overall, similar survey response rates for reporting damage levels,
42 % and 45 %, were achieved for housing units located within and outside of the inundationarea, respectively.
Table 2-2. Residential Building Damage.

None
Minor
Moderate
SevereComplete
Missingn=No.
housing
units
162
45
54
47
1
1
310

%
52
15
17
15
0
0

Damage was evaluated for the 310 units through a discussion with the respondent, where thesurveyor asked the respondent to describe the damage experienced, including the depth of theflood water inside the home and the types of repairs required. The surveyor then showed thedamage category card (see Appendix 2B) to the respondent and confirmed the category thatmatched their description. Damage categories included none, minor, moderate, severe, and31

complete. The full description of these categories is provided in Appendix 2B and is identical towhat was used in Wave 1. Of the 100 sample units in the inundation area that indicated whether
or not their home was damaged, 9 % had no damage, 25 % reported minor damage, 27 %reported moderate damage, 37 % reported severe damage, less than 1 % (one respondent)
reported complete damage, and less than 1 % (one respondent) did not indicate the level ofdamage (reported as missing in Table 2-2). Of the 210 sample units outside of the inundation
area who provided information on the initial damage to their home, approximately 73 % reported
no damage, 10 % reported minor damage, 13 % reported moderate damage, and 5 % reported
severe damage.
Dislocation Time, Impacts, and Decision Making
A total of 296 households reported information on dislocation, including 40 % who indicatedthey did not dislocate, and 60 % who reported that their household dislocated for at least one
day. Table 2-3 provides the number and percent of households dislocated by housing type andtenure, as well as the average and median number of days dislocated for each category. As
shown in Table 2-3, 57 % of households living in single-family dwellings dislocated; 59 % ofhouseholds living in multi-family dwellings dislocated; all households whom we surveyed living
in duplexes or mobile homes dislocated; 75 % of renters and 56 % of owners dislocated. Thus, as
past disaster studies have highlighted, MFD residents and renters were more likely to dislocate.
Larger multi-family dwellings (with three or more units) experienced the longest averagedislocation time, followed by single-family dwellings although the median days of dislocation
were similar for all categories. In Table 2-3, average and median day estimates do not include
those households who did not dislocate or who are still dislocated. It is important to note that the
average dislocation time for homeowners (93 days) was over twice as long as the averagedislocation time for renters (39 days). This finding is counter to what has been commonly
observed in the literature from past hurricane and flood events, where renters are typicallydisplaced for longer periods of time than homeowners. The differences are explained through areporting of the difference in number of homeowner versus renter respondents, and the mediandays of dislocation. Overall, there were nearly three times more owners who responded to thedislocation question, and the median dislocation time was one day shorter for renters thanowners.
Table 2-3. Household Dislocation Duration Across Housing Type and Tenure for Households
who Dislocated and Returned Home by Wave 2.8

Housing Type

Tenure8Single-Family
Multi-Family
DuplexMobile Home
RenterOwner

No. HHsdislocated
143
13
20
2
51
127

% HHs
dislocated
57
59
100
100
75
56

Average(days)
86
105
11
9
39
93

Median(days)
7
5
7
9
13
14

Std. Dev.
(days)
126
73
119
14
121
8

The abbreviation HHs is used to denote “households” in the tables displayed in this section.

32

Figure 2-1 provides the distribution of household dislocation time for the 296 households thatreported their dislocation experience by number of days, with each bin being equal to 30 days
and representing dislocation times within the time range labeled on each bin. In total, 40 % of
respondents did not dislocate; 39 % of respondents dislocated for no more than 30 days; and 21% of respondents were dislocated for more than 30 days. Three households (1 %) reported thatthey were still dislocated; this is marked as the ‘More’ bin. Figure 2-1 is characterized by a leftskew in the data where the majority of respondents (79 %) fall into the first two bins.

Figure 2-2. Household Dislocation Time for all Responses.
There are important implications from being dislocated from home, such as household members
missing school and work. Table 2-4 provides the 296 responses to missing work due to housing
issues caused by the flood, where 23 % indicated that someone in their household did miss work.
Of the 296 households who responded, 41 (14 %) indicated that they were retired, disabled, orotherwise did not work, thus accounting for the difference in yes, no, and total responses listed inTable 2-4.
Table 2-4. Missed Work Due to Dislocation.
Yes
No
Not applicable
Total responses

No. HHs
67
188
41
296

%
23
63
14

Figure 2-2 provides the distribution of number of work days missed by the 67 households who
indicated they missed work due to housing issues; 2 of the 67 households indicated that they did
not know how much work had been missed, or missed by a different member of their household.
Approximately 11 % missed a week or less of work; approximately 80 % missed less than amonth; and approximately 20 % missed over a month. Of the five who had missed more than 90days of work, three missed approximately five months, and one missed an entire year. The fifth
respondent, who missed the greatest number of days, was self-employed, worked from a homeoffice, and was still out of work at the time of the survey.
33

Figure 2-3. Number of Missed Work Days Due to Flood-Induced Housing Issues.
If respondents reported that they missed work, they were asked what sector they were employed
in. Of these respondents, 19 % worked in education; 19 % worked in healthcare; 7 % worked inretail; 36 % worked in sales and services; 10 % worked in manufacturing; 1 % worked inconstruction; and 6 % worked for a government entity.
Respondents were also asked a series of questions to determine what external factors they
believed contributed to their dislocation time, including the timing of insurance payouts or thereceipt of other financial recovery resources, closures or other changes with their place of
employment, closures or changes with their children’s school, and closures or changes with otherbusinesses, such as doctor’s offices, pharmacies and grocery stores. Table 2-5 provides the
distribution of these responses. The total responses vary across categories due to not every
respondent (a) receiving an assistance payout, (b) being employed, (c) having children in school,or (d) partial survey completion. Overall, the majority of households indicated that these factors
did not contribute to their dislocation time or decision to return. As used in Table 2-5, the term
‘return’ refers to whether the former household returned to their original home following aperiod of dislocation, whereas ‘re-occupancy’ refers to the status of the housing unit being
reoccupied by any household. A housing unit can be reoccupied by any household; the original
household does not have to return for this status to be achieved. Thus, it is important to note thatthese questions were not asked to new residents who moved into the unit after Hurricane
Matthew (as these households were not “returning”). It is possible that households who did not
return stayed away for reasons other than those assessed by the survey.
Table 2-5. External Influences on Return Decision-Making.
Timing ofAssistanceNo. HHs
%
Yes
No
Total
responses

35
252
287

12
88

Work Changes
No. HHs

%

7
246

3
97

253

Children’s School
ChangesNo. HHs
%
7
92
99

7
92

Changes with
Other Businesses
No. HHs
%
15
276

5
95

291

34

Housing Recovery Progress
Four proxy measures were used to indicate and evaluate progress towards housing recovery in
Lumberton: (1) re-occupancy, (2) repair completion, (3) restored accessibility, and (4) restoredstability. Re-occupancy was evaluated by the interviewer using indicators such as, a car in thedriveway, well-kept lawn, toys in the yard, and cable hook-up, to determine whether the housing
unit appeared to be occupied. Restored accessibility was evaluated by asking the household ifthey had the same access to school, employment, childcare, healthcare, and grocery stores, at thetime of the survey as they did before the flood. Restored stability was evaluated by asking thehousehold if they intended to stay in their current housing unit for at least one year, indicatingthat they had stable housing (Merdjanoff 2015). Table 2.6 reports responses to these four
proxies, where the number of households responding to each question varied due to incompleteor partial responses. If a member of the household or a neighbor was surveyed, then occupancy
status was validated. In Wave 2, 625 (74 %) sample units appeared to be occupied (where manyof these housing units were unoccupied or abandoned during the Wave 1 data collection); 71 %of these data points were validated through speaking with the household, a neighbor, or manager.
Repairs, accessibility, and stability are reported for continuous residents only, where different
numbers of responses were received for all questions. Overall, 64 % who had experienced
damage reported that their repairs were complete, 91 % reported their accessibility had been
restored, and 90 % reported that their stability had been restored. These results show that 74 % of
housing units in our sample had reached re-occupancy and therefore, had made some degree ofprogress towards housing recovery, despite the lower value (64 %) for repair completion.
Meanwhile, 26 % of the sample did not meet any of the metrics employed here for measuring
housing recovery progress at the time of the survey, which took place 14 months after Hurricane
Matthew.
Table 2-6. Progress towards Housing Recovery.

Re-Occupancy
Repair Completions
Restored Accessibility
Restored Stability
None of the above

No. HHs%

Avg. (days)

625
96
267
265
224

74
64
91
90
26

NA1
149
NA
NA
NA

Std. Dev.
(days)
NA
122
NA
NA
NA

1

Not applicable

School Recovery
Based on survey responses, 32 % of households had at least one person of age 18 or younger
living in the household at the time of the survey. If there were children in the household, thesurvey asked if those children were enrolled in school at the time of the flooding: 74 % ofhouseholds with children responded to this question, and 84 % of those households had children
enrolled in school at the time of the flood. When the respondent answered yes to the latterquestion, they were asked a multiple-choice question on their perception of their child/ren’s
status in educational recovery. Table 2-7 presents the results; the majority of parents or othercaregivers indicated a positive trajectory status for the child/ren in their household. From Table
2-7, 10 % perceived their child/ren’s educational situation to be better than it was before the35flooding, 67 % perceived it to be the same as it was before the flooding, 8 % reported theirchild/ren’s education situation was worse than it was before the flood, and 14% reported that it
was uncertain because things were still changing.
Table 2-7. Householder’s Perception of Children’s Educational Recovery.

Better than it was before the flood
Back to where it was before flood
Worse than before the flood
Uncertain
Other1

No. HHs

%

8
52
6
11
1

10
67
8
14
1

1

The answer option of ‘Other’ was used to account for unanticipated circumstances, such as achild/children moving away to live with another family member.

Recovery Resources and Unmet Needs
There are many types of financial resources available to households recovering from a flood,
however some of these resources had not been made available to households at the time of the
Wave 2 survey, and of those that were available, they often took months to be received. Table 28 shows that 78 % of homeowners had homeowner’s insurance, but only 25 % had flood
insurance. Of those, only 23 % received a payout from their homeowner’s insurance claim,
whereas 62 % received a payout from their flood insurance claim. Furthermore, it took nearly
two months to receive flood insurance payouts, on average, and over two months to receive
homeowner’s payouts, on average. Only 29 % of renters had renter’s insurance coverage; 30 %received a payout, and on average, this took five weeks to be distributed.
Table 2-8. Insurance Coverage and Claims Received.

Had coverage
Claims received
Avg. days to receive claim

Homeowner’s
No. HHs
%
177
78
40
23
68

Flood
No. HHs
%
58
25
36
62
51

Renter’s
No. HHs
%
20
29
6
30
38

The proportion of households that applied for federal, local, non-governmental, or private
disaster assistance varied from 8 % to 50. As shown in Table 2-9, the highest percentage (50 %)of households applied to FEMA’s Individual’s and Household’s Program (IHP); as shown in
Table 2-2, 52 % of respondents did not experience damage to their home. There were othersources of recovery assistance available to Lumberton residents after the flooding; 12 % ofhouseholds applied for loans from the Small Business Administration (SBA), 8 % applied for the
U.S. Department of Housing and Urban Development (HUD)’s Community Development BlockGroup Disaster Recovery (CDBG-DR) funds, and 10 % applied for financial assistance from
non-government organizations (NGOs). Households that were renting were not asked about thefunding sources listed in Table 2-9. One respondent did not know whether their household hadreceived FEMA, SBA, or HUD funding.
36

Table 2-9. Recovery Assistance Application.
FEMA
No. HHs%
116
50

AppliedDid not
Apply
Unsure if
Applied
Total
responding

113

SBA
No. HHs
27

%
12

HUD
No. HHs
18

%
8

205

88

214

92

49

NGO funding
No. HHs
%
23
10
211

1

1

1

0

230

233

233

234

90

Table 2-10 illustrates the number of households receiving assistance from each programcategory, including two additional categories: (1) cleanup from NGOs and (2) financial support
from friends or family. These two categories do not appear in Table 2-9 since they were notassumed to be “applied for” as the funding sources in Table 2-9 require. The majority of
applicants reported that they received some amount of funding from FEMA and NGOs. No
households had received support from HUD CDBG-DR funds since these were yet to be
distributed to households 14 months after the initial impact of the flooding. Varying number of
respondents were unsure on the timing to receive any of the surveyed recovery resources. On
average, it took 46 days for households to receive assistance from FEMA, with times spanningfrom one day to one year. On average, it took 100 days for households to receive SBA loans,with times spanning between two days and nine months.
Table 2-10. Recovery Assistance Receipt and Timing.
FEMA

Yes
No
UnsureTotal
responding
Avg. days
to receive

No.
HHs
75
41
1

%
64
35

SBA
No.
HHs
11
1
16

NGO
funding

HUD
%
39
61

No.
HHs
0
9
9

%
0
50

No.
HHs
20
2
1

%
87
9

NGO
Cleanup
No.
HHs
60
173
1

%
26
74

Friends &Family
funds
No.
%
HHs
16
11
132
87
4

117

28

18

23

234

152

46

100

Still
Waiting

107

39

76

Finally, households were asked if the financial support they received from any external source
was enough to cover everything that was damaged or lost during the flood. Table 2-11 reportsthat only 18 % responded “yes” to this question, whereas 79 % responded “no”. Of that 79 %responding no, 84 % reported that they planned to personally pay for the difference.
Furthermore, of those households left with unmet needs, Table 2-12 provides their description of
the amount of coverage provided through external assistance. From Table 2-12, 17 % reported
37

that almost all of the damages and losses were covered, and 22 % and 9 % reported very little
and none, respectively. Similar to above, the differences in number and total responding in Table
2-11 accounts for those respondents who did not know whether everything had been covered andwhether their household was personally paying for the difference.
Table 2-11. Unmet Needs from Insurance and Recovery Assistance.

Yes
No
UnsureTotal responding

Covered Everything
No. HHs
%
23
18
103
79
4
130

Personally Pay for Rest
No. HHs
%
81
84
10
10
6
97

Table 2-12. Coverage from Unmet Needs from Insurance and Recovery Assistance.
None
Very Little
Some
Almost All
Still Waiting
Don’t Know
Total responding

No. HHs
9
23
51
18
1
2
104

%
9
22
49
17
1
2

Demographics
Lastly, the demographic characteristics of the households responding to the survey are shown in
Table 2-13, which provides the racial makeup of the respondents’ household. The respondents
were asked to self-identify the race and ethnicity of their household. The values in Table 2-13 aresimilar to those for the City of Lumberton, but differ accordingly for Robeson County, and Stateof North Carolina, as reported in the American Community Survey (ACS) five-year estimates for2016 (ACS 2020). For example, White (alone) is reported as 36.4 %, 26 %, and 64 % for theCity, County, and State, respectively, compared to 38 % in the Wave 2 data.
Table 2-13. Race and Ethnicity of Survey Households.
White
Black or African American
American Indian or Native American
More than one race
Other
Hispanic or Latino
Missing

No. HHs
125
103
38
24
10
8
26

%
38
32
12
7
3
2

38

Table 2-14 provides the maximum educational attainment of any individual residing in thehousehold. The values in Table 2-14 are similar in some cases and different in other cases
compared to ACS data. For example, the ACS five-year estimates for 2016 report 23 %, 23.6 %,and 13.7 % for the City, County, and State, respectively, for a percentage of population with lessthan high school education, which is much higher than the 6 % of respondents surveyed in Wave
2. However, ACS reports 30.9 %, 34.4 %, and 26.4 % for the City, County, and State,
respectively, for the percentage of population with a high school diploma, which is similar for
Lumberton as measured in Wave 2 (26 %).
Table 2-14. Maximum Household Education Level of Survey Households.
Less than high school
High schoolSome college
Associate's degree or technical school
Bachelor's degree
Master's degree or higher
Missing

No. HHs
21
85
55
21
66
48
30

%
6
26
17
6
20
15

Table 2-15 provides the household’s combined annual income. The values in Table 2-15 aresimilar to ACS data for Lumberton. For example, the ACS five-year estimates for 2016 report
19.5 %, 15.8 %, and 7.7 % for the City, County, and State, respectively, for households earning
less than $10,000 per year, compared to 17 % recorded in Wave 2. Similarly, the ACS five-year
estimates for 2016 report 6.5 %, 8.5%, and 11.6 % for the City, County, and State, respectively,
for households earning between $75,000 and $99,999, compared to 5 % recorded in Wave 2.
Overall, demographic representation in Wave 2 was reasonably consistent with demographic
representation in Lumberton.
Table 2-15. Annual Household Income of Survey Households.
Less than $10,000$10,000 to $19,999$20,000 to $29,999$30,000 to $49,999$50,000 to $74,999$75,000 to $99,999$100,000 to $149,999
More than $150,000
Missing

No.
57
40
35
33
33
16
26
26
60

%
17
12
11
10
10
5
8
8

39

Chapter 3: Business Interruption and Recovery
3.1 Goals and Objectives
Similar to returning people to their homes, restoring the local economy following a disaster is
one of the most important collective goals for a community. As highlighted in the introduction,in Wave 2, the field study expanded to include a new business-focused data collection effort withthe primary goal being to document impact, interruption, and the early stages of recovery for
Lumberton businesses. The business component was added to support on-going research in theCenter and at NIST which includes the interdependency between housing and business recovery.
Since business impacts were not captured in Wave 1, additional background and context isprovided in this Chapter. The specific objectives of the business survey efforts include:
1. Collecting data on initial damage to businesses after Hurricane Matthew that was not
systematically captured during Wave 1;
2. Obtaining data on the duration of business interruption and/or closure considering
different initial damage levels, ownership structures, industry, available recovery
resources, and accessibility issues;
3. Developing a better understanding of what interdependent infrastructure and services
contributed to business closures and re-openings;
4. Obtaining business-level data on a range of factors expected to influence businessinterruption and recovery, including recovery policy and finance programs.
Research on business recovery in the United States at the individual business level tends to focus
on long-term recovery (Marshall, Niehm, Sydnor and Schrank 2015; McDonald, Florax and
Marshal 2014; Webb, Tierney and Dahlhamer 2002). Fewer studies focus on short-term businessoutcomes (Dahlhamer and Tierney 1996; Xiao and Van Zandt 2012), with some existing studies
utilizing convenience or representative samples as opposed to a randomized sampling strategy(Corey and Deitch 2011; Lam, Arenas, Pace, LeSage and Campanella 2012; Lesage, Pace, Lam,
Liu and Campanella 2011). This study contributes to the literature by using a random sample
research design to investigate short-term recovery outcomes, of which the resulting model can be
integrated with infrastructure restoration models and building recovery models to forward
community resilience research.
Business Damage. Physical damage is a primary cause for business interruption in the aftermathof a disaster. This includes damage to the building, damage to the equipment and machinery, anddamage to contents and merchandise. During Wave 1, the widespread damage to the community
led to one team branching off to investigate damage to businesses and school buildings as well(see Chapter 4 in van de Lindt et al. 2018). Wave 2 includes the business sector as a primary
focus alongside housing, and accordingly, developed a new set of damage states specific forbusinesses (see Appendix 3B). During Wave 2, initial damage was no longer apparent. Therefore(and similar to what was done for housing in Wave 2), the survey was used to ask respondents
their perception of initial damage to the building, equipment and machinery, and contents andmerchandise given the defined damage states (see Appendix 3B).
Business Recovery. The literature on business recovery often uses two major dependent variable
categories: (1) recovery as a binary state, i.e. recovered or not recovered (Dahlhamer and Tierney40

1996; Xiao and Van Zandt 2012) or closed or open (Marshall et al. 2015; McDonald, Florax, andMarshal 2014), or (2) recovery as a scale, for example the business is better-off, worse-off, or thesame (Dahlhamer and Tierney 1996), if it sees an increase or decrease in business (Corey andDeitch 2011; Dietch and Corey 2011; McDonald et al. 2014), or an index of several factors(Webb, Tierney and Dahlhamer 2002). The team found no literature that explicitly examines therelationship between these metrics, or if one is preferable to the other. Thus, the Wave 2 survey
was designed to capture several measures of recovery to better understand this research question.
Summary statistics are presented in this chapter while detailed variable correlations, factor
analysis, and other types of analyses are being published in other documents authored by Centerand NIST researchers with links available on the Center website (http://resilience.colostate.edu/).
Business Recovery Resources. There are fewer recovery resources available to businesses
compared to households. Federal low interest loans are made available to qualifying businesses,
although local and family-owned small businesses often struggle to qualify for these types ofloans after disasters because they have lost too much to be able to incur additional debt
(Dahlhamer & Tierney, 1998). The Wave 2 survey systematically captured what resources wereavailable to businesses after Hurricane Matthew, including whether the business applied, and thetiming of receipt. The data collected in Wave 2 will enable integrating policy options into
recovery analyses, including better understanding whether a business that receives financial
assistance is more likely to recover than one that does not (Watson et al., 2020).
Business Interdependencies. Similar to physical damage, building functionality andaccessibility are critical for a business to remain in operation (e.g., water, electricity, street andsidewalk access). Information was systematically collected on utility outages, duration ofoutages, and accessibility issues. This information will enable further business interruption andrecovery integration with sewer, landline and cell phone service, internet, and water. In addition,
conducting this research in tandem with the housing survey furthers modelling work regarding
the interdependence between housing and business sectors in community recovery.

3.2 Sampling procedures
Since Wave 1 did not draw a random sample of businesses, this sampling was performed during
Wave 2. The field team used the ReferenceUSA database (InfoGroup 2016), provided by an
InfoGroup company of the same name for subscription by libraries, academic institutions or
government agencies to draw the business sample. Only businesses that were “verified” byphone calls made by ReferenceUSA were considered to reduce the error inherent in the database.
As a starting point, 2,547 records of businesses that existed in Lumberton, NC in 2016 were
downloaded. From there, non-profits, government agencies, and other businesses that do not
generate profit were excluded. This brought the number of records down to 2,017.
To begin, the inundation area as well as the FEMA 100-yr floodplain for Lumberton was used to
select businesses for the sample. Using ArcGIS, all businesses that fell within the inundation
area—or a 100-m buffer around it—were included in the sample (n = 218). Additional random
sample of businesses in the northern portion (see Figure 3-1) of the floodplain were identified,reaching a total sample of 350 businesses, proportioned in a similar manner to the housing
sample. Sampling from both the inundation area and the floodplain provides more observations
and thus more statistical power in ensuring that the analysis is representative of the community41

of Lumberton as a whole. In addition, this sampling strategy captures businesses that were
physically impacted as well as businesses that might have escaped physical damage but faced
interruption to their operations due to utility and customer disruption. Overall, capturing bothtypes of impacts is imperative to understanding community-level impacts from businessinterruption and closures.
In the field, errors in the ReferenceUSA data (e.g., businesses in the sample that closed prior to
Hurricane Matthew or incorrect listings) were found. These inaccuracies in the sampling frame
resulted in a smaller than anticipated sample. Therefore, the team decided to supplement theoriginal sample by drawing an additional sample of 103 businesses to serve as substitutes forineligible businesses and to reach the desired response goal of 350 businesses. The additional
103 businesses were randomly selected from the northern floodplain area because all businesses
in the inundation area had been previously included in the sample. Seven businesses in the field
were surveyedin place of the original business if the new business was not at that location at thetime of Hurricane Matthew, bringing the additional sample to 110. The final sample distributed
across the different North American Industry Classification System (NAICS) 2-digit sectors ispresented in Table 3-1.

Figure 3-1. Business Sample Map.
It should be noted that ReferenceUSA lists multiple surgeons and doctors under each medical
office as individual, unique business records. For this study, medical professionals were
excluded from the northern floodplain (i.e., non-inundated) random sample. Medical
professionals in the inundated area were included so they are represented, but it is believed that42

medical professionals, surgeons, and doctors do not have an organizational structure similar toother businesses. They jointly make hiring decisions for staff, but are listed as individual
business entities; therefore, measurements for recovery (i.e., employment growth or decline)
might not be suited for this particular sector. The sector breakdown of the businesses is provided
in the Table 3-1. Overall, there is a good representation of business sectors; the few that areunder- or over-represented compared to other sectors are also under- or over-represented in
Lumberton as a whole.
Table 3-1. Sector Distribution of Sample.
2-digitNAIC
S code
11
21
22
23
31-33
42
44-45
48-49
51
52
53
54
55
56
61
62
71
72
81
Total:

Sector Description
Logging
MiningUtilities
Construction
Manufacturing
Wholesale
RetailTransportation/warehousing
Information
Finance/insurance
Real estate/rental
Professional, Scientific, and Technical Services
Management [1]Administration
Educational services [2]Health care and social assistance
Leisure and Hospitality [3]Accommodation and Food Services
Other services

Original
Sample
%
Total
0.3
1
0.0
0
0.6
2
4.3
15
6.9
24
2.3
8
25.7
90
1.1
4
2.9
10
7.7
27
6.0
21
4.3
15
0.0
0
2.9
10
0.3
1
4.9
17
0.0
0
17.1
60
12.9
45
350

Additional
Sample
%
Total
1.0
1
0.0
0
0.0
0
3.9
4
3.9
4
1.0
1
28.2
29
0.0
0
4.9
5
12.6
13
10.7
11
5.8
6
0.0
0
1.0
1
0.0
0
10.7
11
1.0
1
11.7
12
10.7
11
110

Notes:[1] No businesses in Lumberton representing this sector[2] Only 2 businesses in this sector in Lumberton[3] Only 11 total businesses in this sector in Lumberton

3.3 Survey instrument
The fact basis for the survey was based on a systematic review of the business and disaster
literature. A conceptual model summarizes the factors influencing business recovery in Figure 32.
43

Figure 3-2. Business Outcome Conceptual Model.
As illustrated in the conceptual model, (Figure 3-2), business outcomes such as interruption andrecovery times are affected by a variety of factors. Beginning at the left, the hazard intensity
impacts crucial business inputs such as utilities, customers, suppliers, and labor, and its capital
inputs through physical damage. Business characteristics and management, indicators of its
general performance and function, will also affect interruption and recovery. Lastly,interventions in the form of policy and financial assistance can contribute to recovery after the
initial impact. The survey design, therefore, provided a balance between the research objectives
and the conceptual model, taking into consideration the other focus areas in the field study andsurvey length. The full survey can be found in Appendix 3A.
The survey instrument consists of five major component sections: (1) damage and interruption,(2) business information, (3) business recovery, (4) recovery finance, and (5) demographics.
Damage and Interruption. The damage and interruption section includes questions forbusinesses about physical damage suffered, utility disruption, and the length of, and factors
contributing to, interruption to the business function. This section, therefore, includes variables
related to external factors in the conceptual model, as well as the hazard intensity and impact on
business capital resources. Questions in this section specifically address the labor and customer
inputs by asking about issues related to employees following the flood and the business percent
loss of customers during recovery. The damage states were consistent with the Wave 1
designations, so that future models could be integrated. The damage state designations forcommercial structures and their component characteristics can be found in Appendix 3B.
Business Information. The business information section collected the characteristics of thebusiness, including: size, sector, and age of the business.
Business Recovery. The business recovery section follows and poses various questions on how
the business was doing during recovery compared to before the flood. These recovery measures
include: profitability, capacity, and owner/manager perceptions of recovery. Asking a variety ofquestions to understand different measures of business recovery addresses our second objective.
44

This section also includes a question on the impact to the business’s suppliers as well as collects
information on business capital.
Recovery Finance. The recovery finance portion asks businesses what type of flood insurance
coverage they had, and whether they applied and/or received financial assistance from private
insurance, government assistance, or other sources. These questions also consider temporalelements by asking how long it took for relevant funds to be received. These questions represent
the intervention of policy in the conceptual model.
Demographics. The survey concludes by gathering demographic information on the respondent.
Questions address the respondent’s management experience, age, race, education and ethnicity.
These represent more general business characteristics in the center of the conceptual model aswell as management characteristics.
Prior to use in the field, the Wave 2 survey went through a comment period by Center and NIST
researchers to ensure the business data collection was usable across teams. Particular care wastaken to design the survey so that it could facilitate collaboration with the housing survey team,as housing is easily linked with business outcomes through the labor and customer componentsof business functionality. The final business survey instrument was approved for the Paperwork
Reduction Act (PRA) and by the Institutional Review Board (IRB) at Colorado State University(see Section 2.3).
Most of the survey questions were designed to be unambiguous and easy to understand, but somerequired specific mention during surveyor training sessions. For example, when asking aboutloss of customers, businesses were asked to report their customer loss “after the event”. In
addition, the survey asked specifically about flood insurance, so surveyors were instructed toreiterate that distinction if businesses reported on other insurance coverage. Lastly, business
respondents were instructed to include themselves when reporting employment numbers, which
can sometimes be overlooked when the owner is the sole employee.
Preparation was done before going into the field to save time and allow data collection to run
more smoothly. Once the sample was generated, the geocoding was checked by hand to makesure that the business location on Google Street View matched the geocoding. This process
supported the development of the field maps used by the business team (see Figure 3-3). In
addition, two training sessions were conducted to familiarize the team members with the datacollection process and the survey instrument.

45

Figure 3-3. Field Map Example.

3.4 Data Collection Methodology
A summary of the data collection process is presented in the flowchart in Figure 3-4. Rather thanhousing ID numbers, business sample units were assigned PINs; PINs were filled out at the topof the survey prior to or upon approaching a sampled business. As shown in Figure 3-4,
businesses were approached in-person and given the option to fill out the paper survey
themselves while the surveyors are present, have the survey read in an interview style while thesurveyors filled out their responses, or have a survey dropped off to be completed at a later time.
The team was instructed to have surveys filled out by a business owner or manager in order to
get accurate information, especially for the business recovery and recovery finance sections. If
no owner or manager was present, this was marked on a worksheet and the teams would return
later when the owner or manager was available. The team found it particularly helpful to, insteadof simply marking a business as a revisit and leaving when receiving a soft refusal, always ask
when a manager will be able to fill it out or when to come back and mark it down on theworksheet. When picking up the survey, teams were instructed to check that all questions wereanswered, and record flood depth if not already done.

46

Figure 3-4. Process Flow for Data Collection Used by the Teams.

On the last few days of data collection (January 19 – 29, 2018), in an effort to improve response
rates the decision was made to always leave a survey and information sheet, even if theowner/manager was not present at the time. It was found that leaving a survey sped up theprocess and made the next encounter easier in terms of introductions (e.g., we could introduce
ourselves on the next visit by noting that “we left a survey,” “some of us came in earlier,” etc.)
and made people more likely to fill it out. When the team knew it was going to be the final
interaction with the business and they had not filled out the survey yet, we asked businesses tocall or email one of the team members with their survey responses.
3.4.1 Daily Operations
Zone Assignments. The business sample was divided into geographic zones based on thepredicted availability of the team; this is similar to the clustering performed by the housing team.
A map of the zones is illustrated by Figure 3-3, with the different colors designating the fivezones. When assigning teams to zones, individual team members were kept in the same zone asmuch as possible to help ensure they were familiar with the area and could easily revisit
businesses and keep track of survey completion. Therefore, larger zones were given to people
who were able to spend a longer time in the field (e.g., the full eleven days of the deployment).
The inundated areas were also given priority and made up the two largest zones (the magenta andorange colors in Figure 3-3). A new team member entering the field was always put in a groupwith someone who was familiar with the area and the data collection process. An example of theschedule is illustrated in Table 3-2, where P1 indicates person 1 and so forth. The left mostcolumn of Table 3-2 indicates colors associated with each zone that correspond to the coloredmarkers in Figure 3-3. The final two days of in-person data collection required a slight47

modification of the zones to accommodate and rebalance with the additional sample. Use wasmade of polygons in a new layer to quickly designate the new zones, similar to the techniqueused in the housing survey. Because the zones are meant to improve the field team’s efficiency,
modifying the zones with the remaining businesses requiring a visit allowed for a higher numberof visits per team. More detail on the technology used in the field study can be found in
Appendix 5A.
Table 3-2. Example of Team and Zone Assignments.
Monday(Jan. 22)P1
P2
P5

Zone 1(orange)
Zone 2 P4(red)
Zone 3 P6(yellow)
Zone 4(blue)
Zone 5(green)P7

P3

Tuesday(Jan. 23)
P1
P2

P8

P4

P5

P3

Wednesday(Jan. 24)
P1
P2
P3

Thursday(Jan. 25)
P1
P2

P8

P4

P5

P4

P3

P6

P9

P6

P10

P8

P11

P5

P8

Friday(Jan. 26)
P1
P2

P8

P4

P10

P3

P13

P5

P11

P12

Times of Day. There were between 2 to 4 teams per weekday dedicated to business datacollection. The business surveying was focused during the weekdays during normal businesshours, when most businesses are open. This also allowed the housing surveys to be prioritizedwhen households are most likely to be home (e.g., on weekends). Specific attention was taken to
avoid visiting restaurants during peak lunch hours to not interfere with business operations.
Team Composition. On the first day of data collection teams of four were organized into thesame vehicle that would split up into two teams once they parked in a business zone. However,after the first day it was decided that teams of two per vehicle were both safe and efficient for
conducting the business survey, as businesses were farther apart than housing units, and in the
public realm. If two teams needed to be in one car, they would drive to a zone and separate fromthere on foot to maximize the number of businesses reached. However, groups were never fewer
than two people: one person assumed the speaker/interviewer role, while the other person
assumed the role of scribe. Allowing one person to speak, and not write simultaneously provided
a smoother and more personable interaction. Team members could choose whether they wanted
to designate a primary speaker and writer or whether the roles would switch. Teams were also inconstant communication: the speaker communicated whether their speed was slow enough toallow the note-taker enough time to record the answers, and the writer would also step in and ask
any questions that the speaker may have accidentally missed. Team members were recorded oneach survey that was conducted verbally to clarify questions during data entry and for recordkeeping.
Daily Packets and Process. Each morning, the team would meet to train new team member
arrivals, assign teams and zones, and discuss any changes that needed to be made to the field
protocol. Before going into the field for the first time, the team ran through the survey and48

anticipated questions, some of which were addressed in Section 3.3. For example, the types offunding available to businesses, reminded teams to clarify assistance reported by homebusinesses, and reminded the team that businesses were to report their lifetime experience as amanager, not just their time with the current business. It was reiterated that many of thebusinesses had significant challenges, lost revenue, and potentially received no support. Wheninterviewing businesses, teams were encouraged to be friendly, empathetic, and respectful. It wasalso stressed that the importance of sharing understanding of how hard it has been to start torecover, and that the point of this survey work is to help recommend more resources forbusinesses in the future. For these types of businesses, an information sheet was always left withadditional recovery resources.
There were seven essential materials for each team prepared in packets and handed out daily: azone map, worksheet, Google My Maps printout, information sheet, consent form, damage statecard, and survey questionnaires. The teams were also given access to the digital Google My
Maps on their smartphone. The zone map is similar to the Google My Maps in Figure 3-3 but isspecific to each survey zone; it is a printed map that displays each business in the team’s
assigned zone. The information sheet, consent form, damage state card, and survey can be foundin the Appendix.
The worksheet was a list of businesses in the team’s zone, corresponding to a marker on the zone
map. The worksheet provided a table with the business PIN, address, company name, owner’sname and position, phone number, and description of the sector. Blank fields to be filled include
the survey code (indicating whether the survey was completed, needs pickup, etc.), surveyor
notes (for example, when to return or reason for response refusal), date the business was visited,
and the flood depth during Hurricane Matthew. The survey codes used are defined in Table 3-3.
Table 3-3. Survey Codes used for In-Field Business Survey Coordination
Survey CodeDescription

1Completed: confirmed closed business or completed survey

2

Revisit: need to conduct survey

3

Revisit Priority 2: will not be able to complete survey because manager is out of
town; needs time to check with corporate office; other3*

Follow-up: need to Follow up via Phone; left an email to receive a scanned survey
when completed

4

Pickup: dropped off as survey at the business and we need to pick it up once themanager has completed it

99

Remove: remove from sample; ineligible business; closed before the hurricane; could
not locate business in-person

Every day, the teams and zones were written on a large sheet of paper for team coordination.
New worksheets were printed with the previous day’s notes and the names of team members on
the top. This tracked the remaining business codes through the week and reduced the number of
error corrections that needed to be made later on by eliminating handwritten notes about follow
49

up times and contact points, as well as streamlining the field teams’ daily work. Teams
coordinated routes and a plan for the order of businesses they would visit prior to heading out
each day.
The worksheets were organized by address in order to minimize any issues with businesses
occupying the same physical location. Sometimes there were two businesses listed at one
address, for example a convenience store and a gas station may occupy the same address, or acatering business and a full-service restaurant. Teams were instructed to survey both businesses
at the address if the owner was unique for each. If the owner was the same, the owner could
choose which business (s)he preferred to respond for in order to minimize the research burden on
them. In some cases, a business that had formerly been there and then closed was listed at thesame location as the new business (i.e., currently occupying business). In that case, the current
business was asked which business was at the location during the time of Hurricane Matthew. If
it was the previous business, teams would ask if the current business knew what happened to theprevious business and marked the response on the worksheet.
The field study teams would go to each business on the daily list and determine its operating
status and record their observations on the worksheet and survey header. If the business waspermanently closed the team would move on to the next business. If the business was closed due
to business hours, the teams would make note of when to return on the worksheet. If the business
was open, the teams would proceed to attempt to collect the survey data, following consent.
For businesses that appeared permanently closed or if a business was not at the location provided
on the worksheet, it helped the researchers confirm whether or not the business was in thatlocation and open for business before Hurricane Matthew by checking with a neighboringbusiness. If there was no neighboring business, Google Street View and the business’ webpresence was used to approximate its closure date. In the field, these instances were always
coded both “permanently closed” and “other” (with “need to check” in the text entry) in
Qualtrics unless it was certain that the business was permanently closed. Even for thosebusinesses confirmed closed, some research following the Wave 2 data collection was needed todetermine whether the business had also moved. This research was used to ensure that theclosure data was accurate.
Lastly, certain types of businesses presented unique challenges for data collection that led to an
evolution of training throughout the week. These businesses included restaurants, tax businesses,
and large chain stores. As stated previously, restaurants were far more likely to be able to talkbetween 2PM and 4PM when there were fewer customers. Additionally, tax businesses were
often very busy due to the field study dates coinciding with the beginning of the tax season. For
large chains, some managers felt less comfortable than others with filling out a survey when
there is a corporate office. In these cases, it was often possible to visit again the next day andattempt to speak to someone less risk averse. In cases of corporations, franchises, and large
chains, a regional manager or an individual at the corporate level was more likely to haveinsurance and assistance information for the company and that specific location. Due to thedifficulty of contacting these types of representatives, teams talked with store managers if they
worked there before Hurricane Matthew and left disaster recovery assistance information blank ifthey were unaware or unsure.
50

3.4.2 Data Management
In the evening, the entire field study team entered the survey responses that were collectedduring the day into Qualtrics, an online survey platform. This was done so that individuals could
ask questions, confirm handwriting, and validate responses with their other team members.
When entering data, it was decided to always write down exactly what was said by participants
during the survey or what was written on the survey instrument in lieu of making frequent coding
decisions. This was done to avoid real-time data cleaning decisions while pressed for time or
tired. For example, often a business would answer in a range (e.g. “10-15 employees”,
established “20-25 years ago”), which we would record directly into Qualtrics instead of having
different groups take the maximum, midpoint, or some variation. Decisions on how these will be
coded were made all at once during the data cleaning process post data collection.
Similar to the housing survey, after the field study all physical data was stored in a locked filecabinet and all electronic media was saved on the password protected computers of the principal
investigators that were kept in locked offices. A linked-list has been created where allidentifiable information was replaced with code numbers. The same codes are used for the fieldnotes and photographs from each site. No names are attached to this documentation. Original
data access is limited to project investigators within the Center and at NIST who have completed
the Institutional Review Board (IRB) training and whose universities have signed theInteragency Agreement (IAA). The data will be maintained for the three-year archive period
following the conclusion of the study and archived with NIST at the end of that period.
3.4.3 Follow-up Work: Phone Calls and Determining Closure
When the study team left the field, there were approximately 150 businesses that could still havepotential follow-up. This included businesses that had not completed surveys before the fieldstudy team left Lumberton, businesses whose managers or owners were out of town, or othersimilar reasons for not filling-out the survey. The team decided to address these remaining
surveys through follow-up phone calls and translating the paper survey to an online version. The
phone number that was publicly available through ReferenceUSA was used to contact businesses.
When contacted, the business was given several options for participation. The first option was toverbally go through the survey over the phone while we record their responses. If the businessalready had a survey dropped off with them, we would encourage sending it through the mail to
NIST or scanning it and sending it to the email address of principal investigators. Lastly, wegave an option to complete the survey online if the business was willing to provide us with abusiness/work email. This additional methodology required an amendment to the original IRB
submission through Colorado State University. The calls took place during the month of April
after this amendment was approved. Of the approximately 150 businesses that were called, none
requested that the interview be done over the phone and 22 were willing to give an email addressfor an online version, but the team was only able to get one additional response completed. This
underscores the effectiveness of conducting the business surveys in-person rather than by phone.
Additional research was conducted to determine whether businesses that appeared closed in thefield: (1) had closed after Hurricane Matthew or (2) had moved to another location. Thesebusinesses were copied to an additional spreadsheet where various forms of evidence weregathered. This includes the current status of the record in ReferenceUSA, notes from the field,
Google images, news articles, Facebook pages and other official business web pages. From51

there, two researchers made independent judgements about the status of the business. This
process allowed us to have additional confidence in the validity of the field observations given
the amount of ineligible businesses in the original sample. Once data collection was complete,
the data was then cleaned and coded (as described in the next section).

3.5 Survey results
The data for business recovery were analyzed independently and will later contribute to abroader suite of analyses as part of the longitudinal study for Lumberton, NC. This section
presents the business survey summary statistics resulting from Wave 2. More detailed,comprehensive analyses are being performed and presented outside of this report.
3.5.1 Data cleaning
The data cleaning process was completed using a combination of Qualtrics, Stata, a statisticalsoftware tool, and Nesstar, an open-source software that creates codebooks following Data
Documentation Initiative (DDI) data standards. As described previously, the business survey
team decided to always write down exactly what was said during the interview or what was
written on the survey and then electronically record this response to avoid real-time data cleaning
decisions. Therefore, these decisions were documented at the post-field data cleaning stage of theresearch. The data was first downloaded from Qualtrics and imported into Stata, and a
description of any decision that was made was attached as a note for that particular variable.
Nesstar, because of its ability to import Stata data (.dta) files and the associated variable
information, reports the notes in the final codebook under Variable Descriptions. Here, users
could find the variable, cleaning notes, associated survey question, variable type, and a datasummary of the variable, where applicable. Coding and data cleaning information is therefore
easily accessible and transparent for those using the data. The data cleaning took place in thefour months following the field study to take advantage of the surveyors’ fresh memories; thepaper surveys were consulted for outliers, typographical issues, or any input that needed to be
double-checked.
Some data cleaning decisions were overarching across multiple variables, whereas some were
specific to particular survey questions. The business protocol was similar to the housing protocol
when it came to temporal variables, where months and weeks were converted to days. The
midpoint was taken for all ranges that were reported, with the exception of two-year age ranges,
where the lower value was taken. If a survey question had an “other” option, one variable
captures these responses as a count (e.g. a numeric/categorical variable with an example code of
1=yes, 2=no, and 3= other) and the same variable with a “_txt” suffix will follow that captures
the write-in response (e.g. a string variable with “yes” “no” and “other: sometimes”). Again, theteam recorded whatever was said during the interview, so often some judgements had to be madeon unclear responses. Some example include “a few inches” for flood depth (coded as threeinches), “a couple days” for days of electricity loss (coded as two days), and "water on but
muddy for 2 weeks" for water loss (coded as fourteen days, since the water was unusable). Thiswas most notable in the business age variable, where respondents knew the general timeframe
but not the exact year of business establishment. More specific cases can be found in the notes
for each variable.

52

There were some groups of questions that had particular data coding decisions due to the survey
design. When asking businesses who are both the owner/manager and sole employee about
employee issues, they could not answer the question. Therefore, all employee issue variables
were coded as “missing”, since in these cases the respondent did not or was not instructed toanswer for themselves. Two questions asked businesses about changes in the spatial location of
their customers and suppliers as a Likert scale, which we noted as a somewhat challenging
question for businesses in the field. This was coded as a categorical variable with five categories,
but the team suggests that it might be better suited to three categories based on fieldobservations. For financial questions such as insurance or state, local or federal assistanceprograms, follow-up questions were coded as missing (not applicable) if the respondent
answered "no" to the previous question. For example, if a business responded that they did nothave insurance, the code for whether they filed a claim is coded as “missing/not applicable”
rather than “no” to capture that nuance, particularly if the question was summarized out ofcontext of the previous question.
Lastly, the additional 103 businesses added to the sample (see Section 3.2) have five-digit
identifiers, whereas the original sample of businesses has a four-digit identifier to facilitate thedistinction between the original and supplemental sample.
3.5.2 Survey Response Rates
In total, 164 completed surveys were received, three of which were from businesses that wereclosed or in the process of closing. An additional 55 businesses were closed and 10 had moved,
so a survey could not be completed for them. The final response rate, considering closed andineligible businesses, was 52 %. The final number of eligible businesses, of the original sample
and additional sample with the ineligible businesses removed, is presented Table 3-4.

53

Table 3-4. Sector Representation of Original Sample, Ineligible Businesses, and Final Number of
Eligible Businesses.
2digit
NAIC
S
code
11
21
22
23
31-33
42
44-45
48-49
51
52
53
54
55
56
61
62
71
72
81
Total:

Ineligible

Sector Description

Original +Additional
SampleLogging
MiningUtilities
Construction
Manufacturing
Wholesale
RetailTransportation/warehousing
Information
Finance/insurance
Real estate/rental
Professional, Scientific, Technical Services
Management
Administration
Educational services
Health care and social assistance
Leisure and Hospitality
Accommodation and Food Services
Other services

%
0.4
0.0
0.4
4.1
6.1
2.0
25.9
0.9
3.3
8.7
7.0
4.6
0.0
2.4
0.2
6.1
0.2
15.7
12.2

%
1.3
0.0
0.0
2.5
7.5
3.8
22.5
1.3
7.5
13.8
11.3
2.5
0.0
5.0
0.0
8.8
0.0
5.0
7.5

Total
2
0
2
19
28
9
119
4
15
40
32
21
0
11
1
28
1
72
56
460

Total
1
0
0
2
6
3
18
1
6
11
9
2
0
4
0
7
0
4
6
80

Total
(Original Ineligible +Additional)
Tota
%
l
0.3
1
0.0
0
0.5
2
4.5
17
5.8
22
1.6
6
26.6
101
0.8
3
2.4
9
7.6
29
6.1
23
5.0
19
0.0
0
1.8
7
0.3
1
5.5
21
0.3
1
17.9
68
13.2
50
380

The sector distribution of the observed and collected sample is detailed in Table 3-5. As
indicated by the highlighted cells, there is some difference in sectoral distribution in the surveyedbusinesses compared to the combined original and additional samples in Table 3-4. There is aslight over-representation in retail businesses (NAICS code 44-45) and accommodation and foodservice businesses (NAICS code 72), and a slight under-representation of businesses in healthcare and social assistance (NAICS code 62). This might be due to the walk-in availability ofthese types of businesses.

54

Table 3-5. Sector Representation by Data Collection and Operating Status.
2-digitNAIC
S code
11
21
22
23
31-33
42
44-45
48-49
51
52
53
54
55
56
61
62
71
72
81
Total:

Sector Description
Logging
MiningUtilities
Construction
Manufacturing
Wholesale
RetailTransportation/warehousing
Information
Finance/insurance
Real estate/rental
Professional, Scientific, and Technical Services
Management [1]Administration
Educational services [2]Health care and social assistance
Leisure and Hospitality [3]Accommodation and Food Services
Other services

Total
%
1
0
1
4
6
2
26
1
2
7
6
5
0
2
1
5
3
18
13

Total
1
0
2
17
22
6
101
3
9
29
23
19
0
7
1
21
1
68
50
380

SurveyResponses
%
Total
0
0
0
0
1
1
4
7
5
9
1
2
36
60
0
0
2
4
7
11
4
7
2
4
0
0
1
2
0
0
1
2
1
1
19
31
14
23
164

Observed
Only
%
Total
0
0
0
0
0
0
4
3
3
2
4
3
23
15
2
1
2
1
8
5
2
1
9
6
0
0
2
1
2
1
12
8
0
0
18
12
9
6
65

3.5.3 Findings: Flood Impacts and Recovery
As discussed earlier, the business survey collected information on a range of flood impacts andrecovery measures. This section includes the descriptive statistics collected on damage, utility
and customer losses, business interruption and closures, recovery progress, insurance andrecovery resources, and business demographics for the completed surveys.
DamageThe original sample included 181 businesses in the inundation area. It appears that businesses in
the inundation area had a higher percentage of closures, with 38 of the 181 (21 %) businesses
being closed compared to 20 closures out of the 280 (7 %) non-inundated businesses. In terms ofsurvey responses, 90 of the 164 businesses were located in the inundation area, giving a response
rate of 63 %, while accounting for closures. This is slightly higher than the overall response rateof 52 %.
Because fairly balanced responses were obtained from outside and inside the inundation area,
there was a good distribution of damage responses in the surveyed businesses: 43 % the surveyedbusinesses reported building damage with 50 % reporting contents damage (see Table 3-6).
55

Fewer businesses (26 %) reported damage to machinery. In addition, there were more businesses
reporting minor building damage than any other category, compared to contents damage wheremore businesses reported having complete damage. It is likely that businesses in our sample may
not have all been flooded but were more likely to experience damage related to utility disruption.
Table 3-6. Extent of Damage for Businesses.

None
Minor
Moderate
SevereComplete
Missing
Total reporting damage

Building
No.
91
37
11
0
23
2
71

%
55
23
7
0
14
1
43

Content
No..
80
20
14
13
35
2
82

%
49
12
8
8
21
1
50

Machinery
No.
%
118
72
8
5
5
3
13
8
17
10
3
2
43
26

Utility and customer loss
Utility loss statistics for the surveyed businesses are presented in Table 3-7. There was anextremely high number of businesses reporting loss of electricity and water. Almost our entire
sample (98 %) lost electricity after Hurricane Matthew, and 91 % of businesses lost water
service. Electricity was restored before water on average, however; those businesses that lost
electricity reported being without electricity for an average of 9 days, while businesses that lost
water reported it was unavailable for an average of 14 days. Fewer businesses reported losing
natural gas, while the average length of interruption to the service was greater than for otherutilities. Businesses that lost natural gas service reported not having the service for close to twomonths following the flood. However, 15 businesses reported not knowing if they lost naturalgas, and 56 businesses reported that they didn’t use natural gas in their business. The missing/do
not know and not applicable responses were highest for natural gas. Although not tabulated, 104(64 %) businesses experienced loss of customers, with an average customer loss estimated at 35%.
Table 3-7. Utility Loss.

Electric
Water
Natural Gas
Sewer
Landline
CellInternet/IT% lost

avg.
days

98
91
11
35
67
27
73

10
14
60
34
25
12
16

56

Business Interruption
About 88 % of businesses needed to cease operation for at least one day after Hurricane
Matthew. The frequency of business interruption by number of days is presented in Figure 3-5.
Each bin in the histogram is equal to seven days, or one week.

Figure 3-5. Frequency of Length of Business Interruption (Days).
Most businesses (67 %) closed for fewer than two weeks, with 87 % of businesses able to resume
operations within a month post-hurricane. Those businesses that were closed for longer than amonth had a lot of variation in when they resumed their operation: nine businesses did not reopen for over three months, with three of these nine remaining closed for over six months.
Table 3-8 shows the average days of interruption by type and severity of damage. Generally,days of interruption increased with increasing damage severity to building, contents, andmachinery. With no damage, businesses reported closure times of approximately 10 days,
whereas complete damage caused businesses to be interrupted between to 2 to 3 months. The
standard deviations for interruption times are also the largest for complete damage across thedamage types, indicating a wide range of interruption times in this severity category.
For those businesses that did not sustain any damage, interruption was likely more related toutility outage. Table 3-9 presents average days of outage for each utility and average days ofinterruption for those businesses that lost each utility and suffered no building, contents, or
machinery damage. There appears to be a relationship between damage, utilities, andinterruption. Future analysis of the data will include a more holistic look at the factors explaining
interruption time controlling for utilities, damage, and other factors such as customer loss andemployee issues. Additionally, future analyses will examine the interrelationship and importanceof utilities as a collective since often businesses lost more than one type of utility.
57

Table 3-8. Interruption by Damage.

DamageBuilding
None or N/A
Minor
Moderate
SevereComplete
Contents
None or N/A
Minor
Moderate
SevereComplete
Machinery
None or N/A
Minor
Moderate
SevereCompleteDays of Interruption
Mean
Std. Dev.

No.

10
20
29
92

12
34
24
81

90
37
11
23

9
10
36
22
68

12
7
47
19
75

79
20
14
13
35

10
28
31
70
95

13
21
33
74
79

117
8
5
13
17

Table 3-9. Utility Outage and Interruption in Undamaged Businesses.

Utilities
Electric
Water
Natural Gas
Sewer
Landline
Cell service
IT

Outage days
(no damage)
Mean
Std. Dev.
6
5
11
9
17
15
32
102
7
5
6
7
7
5

Interruption days
(no damage)
Mean Std. Dev.
9
12
9
12
30
29
7
7
9
9
9
9
8
9

No.
66
61
4
21
42
17
47

Recovery
The survey asked businesses several questions intended to serve as indicators of their recovery,
including the percent capacity at which they are operating, their change in revenue since
Hurricane Matthew, and their own perception of their businesses’ recovery. Figure 3-6, a58

histogram of capacity relative to capacity prior to Hurricane Matthew with a bin width of ten,
and Tables 3-10 and 3-11 show how businesses are recovering based on these indicators.

Figure 3-6. Recovery Indicator: Current Operational Capacity.
Table 3-10. Recovery indicator: Perceived Recovery Status.
Recovery Status
Still in survival/response mode
Recovering
Mostly Recovered
Fully Recovered
Still in operation but will never recover
Total responding

No.
9
42
34
72
5
162

%
6
26
21
44
3

Table 3-10 shows that approximately two-thirds of businesses report that they are Fully
Recovered or Mostly Recovered. Figure 3-6 shows many businesses operating at or near 100 %capacity and even reporting an increase in their capacity. However, 24 % of responding
businesses indicated that they were still operating below full capacity. This is slightly different
than the self-reported recovery responses in Table 3-9; although 24 % reported operating below
full capacity, close to 35 % of businesses are still in survival/response mode, consider themselves
still recovering, or believe they will never recover. Additionally, an even greater proportion ofbusinesses, close to 45 %, report a net decrease in revenue since Hurricane Matthew and theflooding based on Table 3-11. This illustrates the importance of multiple measures of recovery
and indicates that a business can return to capacity, but still requires the recovery of its
customers and the surrounding households in order to return to its previous level of profit.

59

Table 3-11. Recovery Indicator: Change in Revenue.
Revenue Change
Decreased greatly
Decreased
Stayed the same
Increased
Increased greatly
Total responding

No.
23
48
55
31
2
159

%
14
30
35
20
1

As outlined earlier in Section 3.5.3, 88 % of businesses ceased operation for at least one day after
Hurricane Matthew. Table 3-12 looks at the relationship between interruption and recovery bysummarizing average days of interruption by the self-reported recovery status of businesses at
the time of the survey. Businesses that report being fully recovered and mostly recovered had thelowest average interruption times. Businesses that reported that they were still recovering andstill in operation but will never recover had higher average interruption times but also had themost variation in their interruption times. Although businesses that reported still being insurvival/response mode had lower average interruption times than businesses reporting“recovering,” there were few businesses in this category. There may also be fuzzy conceptual
boundaries between these categories, which is why there were several measures of recovery.
Table 3-12. Recovery by Days of Interruption.
Recovery Status
Still in survival/response mode
Recovering
Mostly Recovered
Fully Recovered
Still in operation but will never recoverDays of Interruption
Mean
Std. Dev.
27
33
39
56
21
37
16
38
66
68

No.
9
42
34
72
5

Additionally, there may be a clearer association between interruption, revenue, and recovery
rather than directly between interruption and recovery. Table 3-13 summarizes reported revenuechanges after Hurricane Matthew and the average interruption of the business. Businesses thatreported that their revenue decreased since Hurricane Matthew were, on average, interrupted for
longer than businesses reporting their revenue as staying the same or increasing. Businesses thatreported their revenue as staying the same were interrupted, on average, for a shorter time thanthose businesses reporting a decrease in revenue, but for a longer time than businesses that saw
an increase in revenue. This makes some intuitive sense as the longer a business is interrupted,
the more revenue is foregone during this time.

60

Table 3-13. Interruption and Revenue.
Revenue Change
Decreased
Stayed the same
Increased

Days of Interruption
Mean
Std. Dev.
35
52
20
44
14
23

No.
71
55
33

Figure 3-7 then shows a cross-tabulation of levels of recovery and the percent of businesses
within that category reporting decreased, the same, or increasing revenue since Hurricane
Matthew. Figure 3-7 shows that, in general, businesses are less likely to report their revenues
having decreased the closer they are to reporting full recovery. Conversely, businesses reporting
being mostly or fully recovered are more likely to report also having an increase in revenue since
Hurricane Matthew.

Percent ChangeThis analysis looked at interruption, revenue, and recovery, but there are several factors that
affect each of these concepts. Business interruption, illustrated by Tables 3-14 and 3-15, isrelated to damage and utility disruption. Change in revenue may be due to interruption, but canalso be influenced by customer loss, whether the business has other operating locations, andsector of the business. Therefore, multivariate regression analysis in future work is needed tobetter understand these relationships and to control for all the factors presented by the conceptualmodel in Figure 3-2.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Still in operation but
will never recover

Still insurvival/response
mode
Decreased

Recovering

Stayed the same

Mostly Recovered

Fully Recovered

Increased

Figure 3-7. Recovery Status by Revenue Change.

61

Table 3-14. Recovery Status Change.
Recovery Status
Still in operation but will never recover
Still in survival/response mode
Recovering
Mostly Recovered
Fully Recovered

Decreased
100
78
81
36
19

Stayed the same
0
22
14
54
41

Increased
0
0
5
9
40

Total
5
9
42
33
70

Insurance and financial recovery resources
In general, the amount of financial resources available to businesses after Hurricane Matthew
was limited. In terms of insurance coverage, Table 3-15 shows that 68 % of businesses reported
having building insurance, 50 % had contents insurance, and 32 % had interruption insurance.
However, 93 % of businesses experienced interruption, 50 % of businesses reported content
damage, and only 43 % of businesses reported building damage. There is a clear mismatch interms of the event, resulting losses, and the insurance coverage businesses had at the time of
Hurricane Matthew.
Table 3-15: Insurance Coverage.

Yes
No
Total responding

Building
No.
%
68
56
53
44
121
100

Content
No.
63
62
125

%
50
50
100

Interruption
No.
%
36
32
77
68
113
100

Table 3-16 presents the claims filed and percent approval for those claims at the time of Wave 2.
Only approximately a quarter of businesses filed some sort of insurance claim. For thosebusinesses who filed an insurance claim, only 55 % received a payout for their building, 50 %received a payout for contents and 31 % received payout for interruption.

Table 3-16: Insurance Claims Received by Those who Filed.

Yes
No
Total applied

Building
No.
15
11
27

%
56
41
100

Content
No.
13
12
26

%
50
46

Interruption
No.
%
4
31
8
61
13

The amount of businesses that applied for federal, local, non-governmental, or private funding
assistance was low. The highest proportion of businesses that applied for assistance applied to62the Federal Emergency Management Agency (FEMA) and the U.S. Small Business
Administration (SBA); where 15 % of businesses reported applying to each source. The numberof businesses applying for financial assistance is reported in Table 3-17, below.
Table 3-17: Number of Businesses Applying for External Assistance.
FEMA
No.
21
117
138

Yes
No
Total responding

SBA
No.
21
116
137

%
15
85
100

%
15
85
100

Other federal
funding
No.
%
4
3
133
97
137
100

Local
funding
No.
%
1
1
137
99
138
100

NGO
funding
No.
%
1
1
137
99
138
100

Bank funding
No.
%
5
4
131
96
136
100

When looking at the subset of businesses that applied for assistance, an even smaller proportion
received funds. The SBA is the largest provider of assistance to businesses after a disaster andonly 16 % of businesses that applied were issued a loan. Table 3-16 indicates that other federal
funding and bank funding provided almost as much assistance in our sample. Table 3-18
illustrates the number of businesses receiving funding from each program category.

Table 3-18: Number of Businesses that Received External Assistance out of Those that Applied.

Yes

Other federal Local
funding
fundingNGOfundingNGOcleanupBankfunding

%

No.

%

No. %

No. %

No. %

No. %

3

16

2

50

0

0

1

100

5

100

2

40

100

16

84

2

50

1

100

0

0

N/A N/A

3

60

100

19

100

4

100

1

100

1

100

5

5

100

FEMA

SBA

No. %

No.

0

0

No
19
Total
responding 19

100

Demographics
Lastly, we report the demographic characteristics of the owners and managers responding to thesurvey. The average age of respondent was 48 with an average managerial/ownership experienceof 16 years. Table 3-19 illustrated the racial makeup of the respondents and Table 3-20 illustrates
the distribution of educational attainment. According to Tables 3-19 and 3-20, the responding
owners and managers were white by majority (53 % white, 13 % Black or African American,
and 17 % American Indian or Native American). Other races made up less than 10 % of thesample of business owners and managers. The most commonly reported education attainment
was completion of a high school education (37 %), followed by completion of a Bachelor’s
degree (22 %), an Associate’s degree or technical school (14 %), and some college (13 %).

63

Table 3-19: Racial Makeup of the Respondents.

White
Black or African American
American Indian or Native American
Asian
Native Hawaiian or Pacific Islander
More than one race
Other
Missing

No.
87
21
28
10
0
7
6
5

%
53
13
17
6
0
4
4
3

Table 3-20: Educational Attainment of the Respondents.
Less than high school
High school
Associate’s degree or technicalschoolSome college
Bachelor’s degree
Master’s degree or higher
Missing

No.
6
60

%
4
37

23
21
36
11
7

14
13
22
7
4

64

Chapter 4: Public Sector Impacts and Recovery
4.1 Introduction
Disaster impacts are experienced by all sectors of society, including by local, county, state andother public entities. Understanding the response of the public sector to the impacts of Hurricane
Matthew is necessary to understand the recovery process for Lumberton. In Wave 2, the teamreturned to Lumberton to follow up with a number of the representatives from Wave 1 with theprimary goal of documenting the way the public sector is working through the recovery process
and where its representatives identified challenges to moving forward. The objectives of the
Wave 2 community representative interviews and meetings were to:
1. Build and maintain relationships with key stakeholders to facilitate data collection efforts
and document the recovery process into the future.
2. Improve understanding of how administrators in the public school system responded to
the flooding and subsequent challenges.
3. Qualitatively document, from the perspective of school representatives, how students,
staff, and families were impacted by the event and what decisions were made by school
district leaders over the past year to begin the recovery process.
4. Gather information from key stakeholders regarding recovery of the built environment,
including infrastructure and public, residential, and commercial buildings. Also,
document decision making processes by key stakeholders that are influencing community
recovery.
5. Better understand the social context of Lumberton, the economic and political challenges
faced by community leaders over the past year, and the communication between the city,
state, and the federal government.
This chapter provides contextual information for the recovery of select portions of the public
sector that provide services to the community of Lumberton, North Carolina. The public sector
services considered in the field study include K12 education, government, and water
infrastructure.

4.2 Data Collection Methodology
In order to better understand business, housing, and household recovery within the context of thebroader community in which it unfolds, our research team met with key stakeholders at the state,county, and city level. During Wave 1, 22 representatives were interviewed from state and local
government, public works, transportation, faith-based and non-profit organizations, and theschool district. In Wave 2, the field team returned to Lumberton to follow up with a number ofthe same individuals to discuss the solutions that had been considered and decisions that weremade over the past year, and to document progress toward recovery as well as any delays
experienced, such as the timeline for the distribution of recovery funding.
Meetings with key stakeholders provided an important supplement to the survey data collection
efforts and help to understand factors that influence recovery for schools, public works, and at65the community-level. The public sector team conducted meetings with community leaders andschool district officials between the dates of January 19 to 29, 2018.
Prior to re-entering the field for Wave 2, the public sector team developed semi-structured
interview guides to support the meetings with community representatives (see Appendix 4B).
For example, an interview guide focused on school recovery was developed for use with school
officials. Similarly, an interview guide was developed for meetings with water infrastructure
officials. These guides were aligned to address broader themes of recovery, including delays,
resources, and key decisions. The researchers also participated in weekly conference calls withall members of the field study team to solidify the data collection plan, ethics requirements, andfieldwork training. These frequent team calls helped to prepare the researchers and develop a
consistent protocol for meetings with school administrators and community leaders. During thistime, the interview team also contacted key stakeholders to arrange the days and times of the inperson meetings.
4.2.1 Daily Operations
For the qualitative component of the field study, a subset of the research team, the public sector
team, were assigned to schedule and attend meetings with key stakeholders and school
administrators. When not in meetings, these individuals were part of either housing or businesssurvey teams. Meetings arrangements were normally made in advance of the field study,
although some final confirmations occurred while in the field. All meetings were held in person.
Each morning, the public sector team convened to review logistics for the day, which involved
arranging teams, vehicles, and drivers, creating a check-in schedule, retrieving packets of datacollection materials (e.g., interview guides, consent forms, photo release forms, supplemental
information for respondents, personal protective gear), and preparing for the day’s datacollection. When necessary, the public sector team separated from the housing and businesssurvey teams and checked back in with the full team at the designated time during the day. Premeeting preparation included review of information obtained from the previous year, review of
the interview guide, and discussion of key questions and issues to address during the meetingthat would support a broader understanding of community recovery (e.g., plans for public
housing, student displacement and retention, building recovery). Each evening, the full fieldstudy team met to enter and back up data, including audio recordings, meeting notes, and photos.
During this meeting, the team followed up on correspondence to confirm upcoming meetings andreviewed any issues encountered from the day.
4.2.2 Data Collection
Prior to returning to Lumberton for Wave 2 data collection, the public sector team reached out toall 22 of the public sector participants who were interviewed in Wave 1. In addition, to fill gaps
in our knowledge the public sector team contacted individuals who were either unavailable in
Wave 1 or who were identified as being key stakeholders as the recovery process unfolded. As
stated in goal 1 above, key stakeholder meetings helped to build relationships with people whosejobs are critical to disaster recovery. Therefore, the field team ensured that follow-up meetings
were conducted by at least one of the same members of the team that interviewed each person (orgroup of people) in Wave 1. In total, the public sector team met with nine school district
representatives including one superintendent, two principals, two school counselors, three66

administrators, and one custodial staff member during the dates of January 19 to 29, 2018. The
team also met with four city representatives (two government officials and two infrastructure
managers) and four state representatives (three emergency managers and one foundation officer
representing the organization that was selected to support distribution of federal funds). Prior to
conducting interviews, a written consent form, provided in Appendix 4A, was reviewed andsigned by the interviewee when the interview was to audio recorded. A verbal consent script wasused when the interview was documented in written notes only.
School staff interviews were conducted using a semi-structured interview guide (see Appendix
4B), that included questions about disaster impacts, response efforts, organizational decisionmaking, student displacement, and the recovery process for students, parents, teachers, staff, andthe larger community of Lumberton. These interviews provided important context for thehousehold and business survey findings as well as real-time documentation of decision-making
processes that influence recovery outcomes. Each meeting lasted between 30 minutes and one
hour, and interviews were captured through audio recordings and field notes. In most cases, one
researcher conducted these meetings.
Key stakeholder meetings with city and state representatives were also conducted using a semistructured interview guide (see Appendix 4B). Questions were focused on the current status ofoperations, including resilience actions and investments being considered; the process ofdecision-making; delays, challenges and issues in the recovery process; and progress in recovery
for the city and the state. Each of these meetings lasted approximately 90 minutes, on average,
and were documented with detailed field notes instead of audio recordings. In all except one
case, these meetings were conducted by at least two researchers from the field study team.
4.2.3 Data Management
Similar to the housing and business survey data, all interview data was stored in a locked filecabinet and all electronic media was saved on the password protected computers of the lead
investigators that were kept in locked offices. Upon returning from the field, all audio recordings
were transcribed verbatim and written field notes were typed out for analysis. Transcripts andnotes were then uploaded in Atlas.ti, qualitative analysis software. All physical data was deidentified, and a linked-list was created to replace names with code numbers. The same code wasused for the audio recording, field notes, and related photographs from each site. No names areattached to this documentation. Materials provided by the participants (e.g., project lists andbudgets, design specifications, photos) were scanned and saved as supporting data files. Original
data access is limited to project investigators who have completed the Institutional Review Board(IRB) training and whose universities have signed the Interagency Agreement (IAA). The data
will be maintained for the three-year archive period following the conclusion of the study.
4.2.4 Data Analysis
The purpose of stakeholder interviews in Wave 2 was to continue to build relationships andsupplement survey data collection with personal narratives and contextual information aboutcommunity recovery. As a result, the interview transcripts and field notes were reviewed andcoded for important information about the community context and specific disaster recovery

67

processes. The meeting and interview data were triangulated9 through the analysis of news
stories, peer-reviewed and gray literature, and findings from the survey data focused specificallyon children, schools, and community recovery. The qualitative analysis, while limited, serves asa basis for new survey questions and associated quantitative analysis in future waves, as well as
semi-quantitative modeling, where qualitative data is used to inform a quantitative model. This isparticularly true as the research team worked to expand on valuable themes that arise from asmall purposive sample to statistically significant findings for a larger, representative sample.

4.3 Findings
The findings of each area of data collection conducted as part of the public sector component of
the field study are presented below. The field team used different approaches to the presentationof findings per section. Section 4.3.1 is focused on education; here, the audio recordings wereanalyzed for themes and quotes are presented alongside discussion of these themes. Section 4.3.2
examines Lumberton’s water infrastructure utility and Section 4.3.3 focuses on the State andLocal Government. In these sections, the meeting notes were similarly analyzed for themes andadditional data sources are used by the team to supplement information provided by the
representatives of the water utility, local government, and state government. In all cases,
triangulation was used to combine the meeting and interview data with other sources of
information, including findings from the survey focused specifically on children, schools, andcommunity recovery, in the analysis.
4.3.1 Education
It is critical that schools are included in studies focused on community recovery and resilience.
In their research on children following Hurricane Katrina and other extreme events, Redlener andSchlegelmilch (2017) assert that following children who have experienced a disaster can serve as
a community's “bellwether of recovery.” Decades of research have shown that displacement or
disruption in education services can have long-term consequences for children’s academic
achievement and delays in reopening schools can prolong recovery for children, staff, families,
and the broader communities where they operate (Fothergill and Peek 2015). Schools are also an
important social institution in communities that are interconnected with other critical community
functions. When schools close, business activities can be disrupted throughout the community
while parents are unable to arrange for alternative childcare. Altogether, it becomes very
important to closely document how child-serving institutions, like schools, prepare for andrecover from disruptive events to better understand the complex process of recovery that
ultimately results in a community’s ability to be more or less resilient to disasters.
The Wave 1 report described the immediate social and physical impacts of Hurricane Matthew
on the community of Lumberton (van de Lindt, Peacock, Mitrani-Reiser, et al. 2018). This
included a detailed account of the physical and social impacts to the Public Schools of Robeson
County, such as damage to school buildings and administrative offices, the evacuation,
displacement, and sheltering of children and families, the initial efforts to reopen schools and
9

The term triangulation refers to the practice of combining multiple sources of data or methods of data analysis toimprove the credibility of a research study. The term originates in surveying contexts where multiple perspectives
are aligned through triangulation to arrive at a more comprehensive understanding (Salkind 2010).

68resume daily district operations, and the challenges that were faced in the first month following
the storm. In this section, findings from Wave 1 are expanded to document recovery decisionmaking processes, and the physical and social recovery of the most heavily impacted Lumbertonschools during the first year following Hurricane Matthew.
Physical Impacts and Response for Schools
In 2016, the Public School System of Robeson County was serving approximately 24,000
students in North Carolina. Flooding from Hurricane Matthew caused extensive damage toschool district offices and impacted eight schools across Lumberton. W. H. Knuckles and West
Lumberton Elementary Schools experienced the most severe damage and all schools were closed
for at least three weeks following the flood. As documented in Wave 1, W.H. Knuckles reopened
after some minor repairs and West Lumberton was closed indefinitely, with students and staff
being displaced to a temporary location at Lumberton Junior High School (van de Lindt,
Peacock, Mitrani-Reiser, et al. 2018).
According to interviews with members of the Public Schools of Robeson County district, in thefirst year following the flood W.H. Knuckles Elementary continued to repair damages to theirflooded building. When the school reopened (on October 31, 2016), they kept the most severely
flooded parts of the building–the gym, the kindergarten/first grade wing, and the cafeteria–closed
for ongoing repairs. Of these, the gymnasium was the first to be repaired and reopen.
Reconstruction on the primary wing took nine months, with kindergarten and first grade students
returning to those classrooms in August, just a few days after classes began for the 2017/2018
school year. The cafeteria reopening followed in October of 2017. During the one year that thecafeteria remained closed, students were served breakfast and lunch in the gymnasium. Food was
prepared off-site and brought to the school each day.
Interviews and news stories revealed that West Lumberton Elementary was never repaired or reopened. The school was completely flooded, and although work began immediately to clean out
the school and prepare for repairs, reconstruction came to a halt when the district realized thatthey may not be able to repair the school because of its location in the floodplain. The cost of
bringing the building up to current standards for construction in floodplains would have been too
high, and it was unclear how much of this cost would be covered by insurance or Federal
Emergency Management Agency (FEMA) assistance.
At the time of Wave 2, the future of West Lumberton Elementary was reported as uncertain.
Students and staff remained displaced at Lumberton Junior High School and the district did nothave plans to repair the school or to reopen it in a new location. However, on June 12, 2018 theBoard of Education of the Public Schools of Robeson County made a unanimous decision topermanently close West Lumberton Elementary School and absorb students into W.H. Knuckles
Elementary for the 2018/2019 school year (Bigelow 2018).
Social Impacts and Response for Schools
As reported in the Wave 1 report, when schools re-opened after 3 weeks of being closed, district
officials were celebrating the level of student retention at their schools (van de Lindt, Peacock,
Mitrani-Reiser, et al. 2018). For example, even though 90 % to 95 % of the students from West69

Lumberton Elementary were still displaced from their homes, 127 out of 151 students returned toschool by the end of November 2016. Representatives from W.H. Knuckles and Lumberton HighSchool reported a similar return to normal student body size immediately following the flood.
However, over the course of the following year, student retention dropped. At the time of Wave
2, officials reported that they had lost over 500 students across the district. West Lumberton
Elementary attendance dropped from 151 to less than 90 students and W.H. Knuckles
Elementary had more than 100 students transfer to other schools. One W.H. Knuckles employee
explained:
Once the flood damage arrived from Hurricane Matthew, we went from a school that
had approximately two hundred seventy-five students, from Pre-K to fourth grade, to aschool of about a hundred and seventy students.
District officials reported that they were not able to track the displacement trajectory ofindividual students as they moved out of the district. At the time of the interview, the district did
not have data showing where students moved when they left their home schools.
The team’s focus on student displacement has revealed how closely school recovery is tied tohousing recovery. The explanation for the attrition of students over the first-year post-flood is asfollows. Initially, when schools reopened three weeks following Hurricane Matthew, parents
needed to get their children back to school. This return to school allowed parents to startassessing their losses and begin their own recovery process. Even if families were living in
temporary shelters or hotels at the time, they were still situated locally, and schools provided
busing from many of the temporary housing facilities. However, as time went on, housing
options became more difficult to find. Families were facing challenges that included completely
flooded homes that were unrepairable, living in unsafe housing that had not been fully repaired,lack of funds to begin and/or complete home repairs, and an immediate shortage in low-income
housing options due to the closure of public housing units and skyrocketing rent. W.H. Knuckles
families were greatly affected by the loss of public housing, as one school representative
explained:
There were four federal housing areas that are closely attached to enrollment at theschool. From November until the Spring, all four of those housing areas were completely
shut down. Even to this day, two of them are back up to almost full capacity and two arestill being worked on. I can't tell you the exact numbers, but a large number of ourstudents came from those four areas.
These challenges forced families to move out of Lumberton to seek housing elsewhere. One
school district representative described it this way:
We had more students return directly after the flood than we realized would come back.
We were truly surprised. As the year wore on we started to see some kids move out ofstate, moving to other parts of the county because there was no, at that point, it didn't
seem like there was any kind of urgency to fix homes or the public housing, which
supplied a lot of the housing for kids that attend school here.

70District representatives indicated that they were concerned about the decrease in funding thatwould be associated with student displacement. Much of a school district’s funding comes
directly from student enrollment numbers that are assessed each year in October. For Robeson
County, funding for the 2017/2018 school year remained stable given that the majority ofstudents returned to school immediately following the floods. However, with the loss of students
over the following year, district representatives anticipate a decrease in funding that would
become effective for the 2018/2019 school year. Such a decrease of funds would greatly impact
the district’s ability to continue recovery projects, invest in mitigation and disaster planning, andultimately would serve as a limiting factor in helping students recover and in protecting themfrom future hazards.
Challenges Remaining for Schools
Since Hurricane Matthew made landfall in 2016, the Public Schools of Robeson County havebeen trying to recover with the resources available. However, with parts of the school district
still operating in temporary locations, post-disaster financial resources still being sought, andwith students and staff still striving to reach stability, many challenges remain. First, at the timeof Wave 2, the school district was still operating out of temporary offices. Although district
officials had been actively working to identify a new location to build a permanent district office,one representative explained the challenges they were facing:
There's a lot of confusion about redoing our admin. You know, several different sides.
Another kicker is when you're looking at trying to stay centrally located… when you lookat Lumberton, the bulk of it is in a flood zone, so how do you plan for that trying toacquire property or find a building that's suitable?
Interviewees also expressed that the absence of a permanent office was difficult for them
personally and made their work lives more challenging, as another district official said:
You don't have a home, because work is your home away from home really. And, youwant to be comfortable in your surroundings. You're really not when you're in a
temporary location. You really don't know about how long you're going to be here or
where you’re going.
Many aspects of reestablishing a permanent district office remain uncertain; these include a safe
physical location for the district office and funding options to cover the rebuilding of the office.
Although the school district was able to begin and complete many of the minor repairs tobuildings across the district, by January 2018 they were still waiting to hear back from FEMA on
an application for assistance, as well as insurance companies who had yet to make a decision
about reimbursement for the loss of West Lumberton Elementary. Delays related to funding were
the subject of a school board meeting in April 2017, during which the interim school
superintendent, Shanita Wooten, told the school board that project worksheets containing
requests for more than $412,000 in repairs to West Lumberton Elementary had been submitted to
FEMA. Approximately 6 months post-flood, the district had only received $164,000 from FEMA
(Hunter 2017). The lack of clarity about the budget for additional recovery projects delayed
action and decision making.
71

A number of challenges related to students were documented. Fifteen months post-hurricane,students were still in need of additional tutoring and counseling services. Counselors reported
that many students at both the elementary and high school levels were still experiencing highlevels of anxiety during inclement weather. One high school counselor explained:
I can see them sitting in a room getting ready to take an important test in biology andlightning pops in the corner and he's distracted the rest of the time now because all he's
thinking about is “I'm ready to go home and hope the flood don't come back.” It's tough.
Although interviewees reported that standardized test scores did not drop following the flood,
they explained that for the students that were already underperforming, keeping up their grades
and schoolwork became even more challenging. When asked what students would need toachieve full recovery, an elementary counselor stated:
I don't know. Just the notion of being fully recovered is kind of foreign to me. If I could
bring all these families out of poverty, if I could bring all these families out of abusive
situations or broken homes, I think that I could see some recovery. It did place a toll on
the community, but there were issues before Hurricane Matthew.
Interviews with local representatives highlighted that the public schools of Robeson County arestill struggling to recover. Student displacement along with unstable and/or decreasing funding
have magnified the preexisting social issues that were already being faced by a district withmany low-income students in need of assistance. Schools can and do support student andcommunity recovery in the aftermath of a disaster if they are provided with the resources to doso. For this school district however, recovery is still unfolding and uncertain.
4.3.2 Water Infrastructure
Restoration of the community’s infrastructure systems is a key part of a community’s recovery
from a disaster. The Wave 1 report provided an accounting of the loss of power and water, withmore attention on the water system given its prolonged recovery time. In Wave 2, the teamcontinued to focus on the water supply system and related recovery decision-making.
This study is timely as water infrastructure is being recognized nationally as needing more
attention in community resilience planning. In an after-action report for Hurricanes Harvey and
Irma, the American Water Works Association (AWWA) recommended elevating water to an
Emergency Support Function at the federal level (AWWA 2018). Additionally, FEMA and theDepartment of Energy (DOE) reports following the active 2017 hurricane season emphasized theimportance of the water sector in community resilience (DOE 2018). New federal legislation(America's Water Infrastructure Act of 2018) authorizes more investment in drinking water
infrastructure resilience to natural hazards. This study thus presents an opportunity to understand
how water system managers prepare for and respond to extreme flooding.
This study also provides an opportunity to understand in detail the interdependencies of criticalinfrastructure at the community level, which has been recognized as an important research andplanning gap. The DOE has noted that the dynamics of cascading failures across various
essential services are “not fully understood, and infrastructure planning does not yet effectively
72

incorporate interdependency considerations” (DOE 2018). Information gathered in thislongitudinal study of Lumberton’s water infrastructure as it relates to impacts, recovery, anddecision-making, including interdependencies with other sectors, will help to close this gap. In
this section, we expand on findings from Wave 1 to document the decision-making processes in
the recovery of Lumberton’s water utility in the first year following Hurricane Matthew.
Impacts and Response for Water Infrastructure
Water service in Lumberton was severely impacted by the flooding caused by Hurricane
Matthew. Lumberton’s water service relied on groundwater (40 %) and surface water (60 %)sources. The sole 16 MGD (60 MLD) with a 24 MGD (90 MLD) capacity water treatment plant(WTP) was built in 1992 and designed to withstand a 100-yr flood (van de Lindt, Peacock,
Mitrani-Reiser, et al. 2018). The water treatment plant was under 4 ft (1.2 m) of water for several
days, compromising both the water treatment and electrical equipment (Whittaker 2017), and thesurface water intake ceased functioning, resulting in a loss of water service to the community.
During Wave 1, the immediate recovery actions taken were documented and the team gained
insight into planning to avoid similar damage from future flooding events. During the Hurricane
Matthew event, water service to the public ceased due to inundation of the electrical control
systems and pumps. When Water World Magazine conducted this 2017 interview with North
Carolina Emergency Management (NCEM) Area Coordinator Eric Wiseman, he described theplant: “When I arrived, here was a plant that had gone from making about 7 million gallons ofwater a day (7 MGD, 26 MLD) to making zero. Nothing was coming out of it whatsoever”(Whittaker 2017). By the time the field study team visited during Wave 1, water service hadlargely been restored. The restoration was achieved with exclusive reliance on groundwater
sources to compensate for the loss of the surface water intake, and with delivery and installationof portable water treatment plant trailers, yielding 6 MGD (22 MLD) of treated water (van deLindt, Peacock, Mitrani-Reiser et al. 2018).
During Wave 1, the team also learned that individual WTP components were being gradually
restored. Various parties (e.g., the city, public works, FEMA, state officials) were discussing
how to guard against another major flood that would disrupt water service for an extended periodof time. Largely, these discussions were limited to the elevation of control systems, theinstallation of a flood gate at the VFW Road and CSX railroad overpass, and placement ofadditional groundwater wells. Important considerations included the type of post-disasterassistance provided, sources of funding, and limitations associated with each. For example,
FEMA communicated that the Public Assistance Program could not be used to fund the floodgate, despite this being the point of failure for the levee. Per the Public Assistance Program
policy, FEMA only provides funding for repairs of facilities that were in existence (including, insome cases, those under construction) at the time of the event (FEMA 2016).
In Wave 1, the team reported that water service restoration was largely dictated by the timerequired to secure temporary water treatment capability. Quick decision-making on the part ofpublic works and other city officials shortened this time. However, in Wave 2, the team wasinterested in understanding the transition from temporary to permanent solutions, as well as the
specifics of the permanent solutions being considered in and around the water treatment plant.
73

Decision-Making and Challenges in the Recovery Phase for Water Infrastructure
By January 2018, the time of Wave 2 data collection, the water treatment plant was operatinglargely as it had before the hurricane. Water treatment plant operators had seen the water demand
return to pre-event levels despite some households having been permanently dislocated.
However, while plant operations had returned to normal in the months following the flood,
public works representatives discussed changes in staffing roles and responsibilities, as well as
the utilization of Lumberton Public Works resources as a result of the flood. Public Workspersonnel, including personnel from the WTP, provided recovery services to the city. For
example, when the city’s official debris removal contract ended, Public Works provided staffing,vehicles, and other resources for debris removal (e.g., as a result of continued building repairs
and construction or tree removal) throughout Lumberton.
The restoration of the water intake and treatment plant components was well underway inJanuary 2018, but mitigation planning had changed. Some alternatives that were underconsideration at the time of Wave 1 had been ruled out and replaced by others. The elevation ofmany of the control systems, for example, had been ruled out on the basis of cost. Instead, theoptions entertained were floodproofing of individual buildings (e.g., pumphouses) andgenerators, and the installation of a berm around the WTP. A flood gate at the VFW Road and
CSX Railroad underpass at I-95 continued to be strongly desired by the City according to Public
Works representatives, even while other options were being pursued between Waves 1 and 2.
Public Works decided to pursue the construction of the WTP berm, based on assessments andrecommendations from FEMA and the input of city officials. The berm was to encircle the WTP;
this included both the WTP and supporting components (e.g., generators, pumphouse). Within
the protection area of the berm a groundwater well would also be installed to ensure agroundwater supply in the event of loss of the surface water intake. In addition, the city
identified a variety of funding sources for flood gates at the VFW Road and CSX railroad
underpass at I-95. The decision to pursue flood gates resulted from active dialogue between the
Department of Transportation, CSX, NC Emergency Management, the city government, and thewater treatment plant.
In addition to the berm and flood gates, water supply and stormwater management decisions
were made to increase the resilience of the local hospital. The attention to the hospital was
prompted by flash flooding during Hurricane Matthew that caused the hospital to be inaccessible
for several hours. The actions to improve accessibility included three miles of stormwaterdrainage improvements in the northern part of town near the hospital. This project was aimed atpreventing a prolonged period of inaccessibility to the hospital in future flood events, though it
was also recognized as providing the added benefit of relief from minor flooding of roadways
from average rain events. This drainage project was funded by the Golden LEAF Foundation.
With the attention on the hospital, a decision was also made to employ an auxiliary well toprovide the hospital with its own backup water in the event of a future emergency.
Challenges Remaining for Water Infrastructure
Many important repair and recovery decisions were at various stages of completion in January
2018. By that time, funding had either been secured or was being identified for key water
infrastructure resilience actions throughout the community. Public processes for decision making
74

regarding these actions were well underway. Despite the progress in enacting decisions,
identifying funds, and developing project plans with input from relevant agencies (e.g., FEMA,US Army Corps of Engineers), there were delays in the use of disaster assistance funding forrestoration, repairs, and flood mitigation. One such example is the time required for constructionof the berm around the water treatment plant. Public engagement challenges, inherent in therecovery process, were also an issue. It is rare for all actions to have complete support from thelocal population or those directly impacted (e.g. public works personnel or city government
staff), particularly when local officials must balance normal operations and capital improvementswith recovery activities. Finally, there was the ongoing challenge reported by Public Worksrepresentatives related to the US Army Corps of Engineers’ certification of the levee, which had
yet to be resolved as of January 2018. Levee certification is the process that deals with the design
and physical condition of the levee and is the responsibility of either the levee owner orcommunity that operates and maintains the levee. Certification must be completed for the leveeto be eligible for accreditation by FEMA, and FEMA’s accreditation affects insurance andbuilding requirements (USACE 2012).
Despite these challenges, fifteen months after Hurricane Matthew impacted Lumberton,
important decisions were being made to increase the resilience of the water infrastructure. This
took the form of increased resistance to future flooding (e.g., the WTP berm and hospital areastormwater improvements) but also increased redundancy (e.g., the additional groundwater wellbehind the berm and the backup water storage at the hospital). In certain cases, there were otherco-benefits unrelated to Hurricane Matthew recovery such as relief from frequent, low severity
flood events that routinely impacted roadways. When considering community resilience, cobenefits10 are the short-term benefits experienced as a result of “investments in financing andresourcing long-term resilience” (Fung and Helgeson 2017).
4.3.3 State and Local Government
In Wave 1, the public sector team conducted meetings with representatives of city and state
government to understand the broader context of the event, its impacts, early response, and toform a better overall understanding of recovery challenges faced by various parts of the city andstate governments. In Wave 2, researchers returned to meet with many of the same
representatives to follow up on the ongoing impacts of Hurricane Matthew and the process ofrecovery, including decision-making, the status of those decisions, and challenges that remain.
Researchers were also interested in identifying the existence of any co-benefits brought about by
recovery actions.
Local Government
The city government, while leading many aspects of the overall recovery of the community, also
experienced significant losses. These losses included public buildings, public utility equipment
and functions, and more than $500,000 in contents. In order to recover these losses, post-disaster
financial assistance and other resources were required. As with individuals and establishments
who sought assistance, the requirements of individual programs dictate eligibility for the local
10This concept is central to the ‘resilience dividend’, which is “the net benefit that accrues from investments aimed
at increasing resilience when a hazard event does not occur, in the absence of a disruptive incident over the planninghorizon” (Fung and Helgeson 2017).

75

government. For example, city officials described that due to a lack of flood insurance coverageon public buildings, FEMA’s Public Assistance Program reduced the funds awarded to the cityby 75% (FEMA 2016). According to city officials, the public buildings did not have insurance
coverage because of the understanding that given the buildings’ location behind the levee, there
was minimal flood risk. Unfortunately, the FEMA funding rule was not widely known by city
officials prior to the event. In addition to the impacts to the city’s public buildings, four ofLumberton’s public housing buildings were also lost (Colman and Cusick 2018). At the citylevel, a range of options were considered to improve the resilience of various sectors, including
water system redundancy, stormwater drainage improvements, purchasing flood insurance for all
public buildings in Lumberton, and creating emergency response plans for future events.
Fortunately, many of the decisions led to recovery actions that provided important co-benefits,
which will be discussed further below.
Impacts and Response for Local Government
Disaster impacts are experienced by all sectors of society, including local government.
Understanding the response of city officials to the impacts of Hurricane Matthew is vital to aholistic understanding of the recovery process for Lumberton. The response included a range ofactions, some of which were led by city officials while others were dependent on external
entities.
Communities often hire consultants to identify solutions and administer complex projects; thisbecomes particularly true following a disaster. This is also attributed to the expertise needed tonavigate the disaster assistance and recovery process, as well as the need to maintain existing
functions while taking on new activities associated with the recovery. Unlike several nearby
communities and schools, Lumberton officials decided not to hire a disaster consultant to support
the application and administration of funds. City government representatives identified that they
perceived this as a positive decision made early in the recovery process and have since learned ofproblems in the communities and institutions where external consultants were employed.
Without a consultant to lead the process, the city directed its own application and administrationof disaster aid. The federal government provides various types of disaster assistance funding,including funds awarded through FEMA’s Hazard Mitigation Grant Program (HMGP). The
HMGP is designed to help communities implement hazard mitigation measures following a
Presidential Major Disaster Declaration. The primary goal is to “enact mitigation measures that
reduce the risk of loss of life and property from future disasters” (FEMA 2018a). In February
2017, initial public meetings regarding the HMGP were held in Lumberton – both in city halland in the community. These funds are often used for a range of mitigation actions including
actions focused on residential structures, as was the case in Lumberton. Actions included
buyouts of homes, elevation of homes, and structural retrofits. In September 2017, eight months
following the initial public engagement, letters of notification were sent out to the property
owners who were deemed to be eligible for these funds. This lag between the public engagement
and the notification letters is indicative of the type of delay, dependent on an external action (i.e.,federal funds had to be awarded to the state and then distributed to impacted communities), thatchallenged city officials in their efforts to support community recovery.

76

Non-governmental organizations (NGOs) often play a significant role in disaster recovery; thiswas certainly true in Lumberton. City government representatives expressed awareness of thetime-limited nature of the services being provided by religious organizations such as the United
Methodist Committee on Relief (UMCOR), Latter Day Saints (LDS) Charities, and NC Baptist
Men. When the field study team was on the ground in January 2018, church organizations werestill working, but the number of volunteers had diminished and there were waiting lists ofhouseholds needing assistance. Most of these organizations were operating on a case by casefundraising method, often raising funds for one household at a time. City officials elected tosupport the ongoing work of these NGOs, recognizing the valuable service being provided to thecommunity along with the absence of alternative service providers. As a result, money the city
received from a crowdsourced fundraising effort was split among the religious organizations thatwere still working in Lumberton several months after the flooding.
Decision-Making and Challenges in the Recovery Phase for Local Government
During meetings with community leaders, recovery delays were attributed to the availability offederal funds, housing repairs, staffing issues, and debris removal. The top item mentioned innearly all discussions with government representatives was the flow of post-disaster assistanceinto the community. The timeline of this flow was repeatedly cited as the factor most impacting
recovery progress in a negative direction.
For housing, the challenge arose with the time required to make housing repairs. Households did
not have funds to make repairs independently and therefore, were waiting on receipt of postdisaster assistance. For the funds to reach households, they first have to move from the federal
government to the state and then to the counties and ultimately to the impacted communities. As
a result, many basic housing repairs were not made by January 2018. When basic repairs are
combined with the costly nature of changes like elevating a structure, the limited ability to
improve resilience by “building back better” is highlighted. A further challenge to home repairs
includes the variation in a local and federal version of the 50 % rule. This rule is used todetermine whether, given the level of damage, a building requires repair or full replacement inorder to resume pre-disaster function. At the federal level, FEMA applies 44 C.F.R. §206.226(f)(1) (FEMA 2018b), which defines the 50 % rule as:(f) Repair vs. replacement.
(1) A facility is considered repairable when disaster damages do not exceed 50 percent of
the cost of replacing a facility to its pre-disaster condition, and it is feasible to repair thefacility so that it can perform the function for which it was being used as well as it did
immediately prior to the disaster.
(2) If a damaged facility is not repairable in accordance with paragraph (f)(1) of thissection, approved restorative work may include replacement of the facility. The applicant
may elect to perform repairs to the facility, in lieu of replacement, if such work is inconformity with applicable standards. However, eligible costs shall be limited to the lessexpensive of repairs or replacement.

77

Alternately, at the local level, Lumberton adopts the state building codes (NC State Building
Code Council 2014) where the 50 % rule is defined as:
For repairs to substantial damage (which immediately equate to substantial
improvement), if the anticipated cost exceeded 50 percent of the building’s market value
before the damage, then all aspects of the entire building, not just portions affected bydamage, need to be upgraded to meet new construction requirements.
City officials expressed that when funds were made available for housing, such as through the
HMGP, the need was greater than that which could be met by the federal funding criteria. For
example, city officials had funds for approximately four times more housing units than would
meet the stringent guidelines of the HMGP. Out of approximately 400 applications, only aquarter were identified in the HMGP. Of these, a little more than a third were approved for
acquisition, and about a third were approved for both elevation and demolition/rebuild.
Funds were also made available for repairs and restoration of public buildings damaged by theflooding. Six million dollars of FEMA Public Assistance funding was awarded by the Golden
LEAF Foundation to the city of Lumberton to address such repairs (see Section on State
Government for a discussion of the Foundation’s role). The repair or replacement of publicbuildings provided significant benefits to the community in terms of modernization and general
improvement to the spaces from which city government services are provided.
Other sources of delay arose from challenges associated with staffing, particularly in areas of
employment related to disaster recovery. These issues included a lack of appropriately trained
staff and the need for financial resources to hire consultants. However, even when funds wereavailable, there was an absence of people with the necessary skills and expertise to serve as
consultants. The shortage of human capital in areas of housing inspection and grants
administration specifically presented significant challenges for those in Lumberton. According tocity officials, additional needs included planners and engineers with experience in the areas ofrehabilitation, elevation, demolition, and rebuild - all activities covered by the HMGP awards.
Finally, incomplete debris removal from contractors required follow-up action by the City tocomplete the process, resulting in longer debris removal times than were anticipated.
Challenges Remaining for Local Government
As is often the case in disaster recovery, the flow of financial resources represents an everpresent concern for local government officials aiming to speed up the recovery of theircommunity. As of January 2018, several major sources of federal disaster assistance had not yet
reached Lumberton. This included CDBG-DR funds through the Department of Housing and
Urban Development (HUD).11 The absence of these funds meant that several identified recovery
activities could not take place. One major exception is the FEMA public assistance program

11

For more information about the process of acquiring CDBG-DR funding, see
https://www.hudexchange.info/programs/cdbg-dr/

78

funds, which were processed quickly, distributed to the state and then to the local communities
via the Golden LEAF Foundation.12
The city officials quickly encountered the lack of flexibility of disaster recovery programs, where
there are strict requirements for the use of funds and what can and cannot be addressed withthose funds. One example of inflexibility was during the debris removal process. Given thestringent requirements of each assistance program, Lumberton’s floodplain manager recognizedthat he must play an essential role. Namely, that floodplain managers must educate municipality
officials who in turn, benefit from improved awareness of the rules and regulations regarding
floods. One such example where knowledge of the rules and regulations would have been
important for Lumberton is with the FEMA penalty for public buildings that lack flood insurance(FEMA’s Public Assistance Program reduces the funds awarded to the city by 75%).
In the midst of these challenges, city government representatives identified several actions thatwould speed up recovery of the community, if the bureaucratic structures were present to support
these actions. The first would be to develop and implement a single application to cover all types
and sources of post-disaster assistance for which an individual or household may be eligible. The
next two actions related to the recovery of housing and the delay it creates for community
recovery. City government representatives identified the importance of improving existing
housing, as opposed to rebuilding and relocation, as one of the most critical aspects of recovering
the community. This recognition arose in part from the understanding of the community –residents wanted to remain in their homes as opposed to moving elsewhere. Likewise, it wascited by city government representatives as critical to repair houses, regardless of ownership, toachieve recovery of the community. Given current limitations in disaster assistance program
requirements, these actions are not possible and therefore, caused significant delays in therecovery process.
State Government
In Wave 2, researchers on the field study team followed up with many of the same
representatives of the state government originally contacted during Wave 1. The aim of thisongoing interaction was to further advance an understanding of the broader context of the event,its impacts, and ongoing response, as well as the process of recovery, including decision-making
and the status of those decisions. At the state level, the team sought to understand how thecommunity of Lumberton was faring in relation to the rest of the state. The challenges being
experienced on the state scale were expected to inform the team’s interpretation of the results of
the household and business surveys at the local scale.
Impacts and Response for State Government
Initially, the state of NC anticipated the flooding of Hurricane Matthew to be comparable to
Hurricane Floyd, but unfortunately it was much worse. Approximately 2,000 evacuations were
issued, fifty counties were declared disasters, and the damage estimate at the time of Wave 2 was$1.5B (NC Department of Public Safety 2016). While a large number of counties in the state had
12

For more information about the process of acquiring FEMA Public Assistance funds, see
https://www.fema.gov/assistance/public79

disaster declarations, 80 % of the funding was being concentrated in the four most impacted
counties – Robeson, Cumberland, Edgecomb, and Wayne. In NC, the state office of emergency
management was responsible for overseeing the distribution of federal disaster assistance funds
from programs such as the Community Development Block Grant - Disaster Relief (CDBG-DR).
Hurricane Matthew impacted many of the state’s lowest-income residents occupying the lowestlying land (Quillin 2016). As a result, a long recovery timeline was expected. FEMA was notable to help persons who did not have insurance. Instead, homeowners in this category often
relied upon donations and faith-based groups for assistance. Renters were another story
altogether. In Robeson County, 18,546 households registered with FEMA; of these, 6,744 wererenters who are not eligible for most disaster assistance funds (Quillin 2017). Further
complicating the recovery process, many of these renters were residents of public housing units
which were lost as a result of Hurricane Matthew. The federal government denied NC Governor
Roy Cooper’s $15.2 million request to repair flood-damaged public housing in May 2017.
According to 2018 Picture of Subsidized Households data, the average HUD Program householdin the City of Lumberton has a household income of less than $10,000 per year (HUD 2018).
Nearly one out of every 10 public housing units in the US are in the combined 100- and 500-year
floodplain designations (104,497 units total) (Furman Center 2017); in North Carolina, nearly 10% of public housing units fall in this category (Colman and Cusick 2018).
The state utilized several partnerships to advance recovery from Hurricane Matthew across theimpacted communities. Of note was the state’s selection of the Golden LEAF Foundation toserve as the subcontractor to the state of North Carolina to manage the distribution of significant
amounts of recovery assistance. The Golden LEAF Foundation is a nonprofit originally founded
in 1999 to use North Carolina’s share of the 1998 settlement with cigarette manufacturers toincrease economic opportunity in the state’s tobacco-dependent, economically distressed, andrural communities (Golden LEAF 2018). The challenges facing these communities such as highcrime rate, high poverty rate, and low educational attainment were substantial before Hurricane
Matthew hit North Carolina (van de Lindt, Peacock, Mitrani-Reiser et al. 2018). In response to
the new disaster facing the state, Governor Pat McCrory and the NC legislature crafted an aid
package to supplement federal and private dollars. They requested that the Golden LEAF
Foundation “take on the stewardship of the resources to aid local government repair andrestoration, and small business disaster loans to fill the gaps unmet by others” (Gerlach 2017).
State representatives described Golden LEAF as an already trusted partner in the impacted
communities, so they were an ideal choice for the State. In addition, the state formalized apartnership with the Voluntary Organizations Active in Disaster (VOADs) to improve
distribution of voluntary assistance to those who need it most right after a disaster.
Coordination was emphasized by several individuals as central to both the response andrecovery. The North Carolina Disaster Recovery Framework was the foundation for the state’s
level of coordination following Hurricane Matthew. For example, the staff conducted regularplanning and tabletop exercises, all plans were already in place, and there was regularcommunication between those who would need to coordinate during the response and recovery
phases. For the recovery phase specifically, strong relationships and an effective Federal
Coordinating Officer were highlighted as necessary components for the progress that had been
made, according to state government representatives. As in Lumberton, the state government isalso working to build resilience for future events. A newly created Office of Recovery and80Resiliency within the Department of Public Safety is just one of the approaches the state istaking.
Decision-Making and Challenges in the Recovery Phase for State Government
The range of disaster assistance funds created their own opportunities and challenges at the statelevel. FEMA Public Assistance funds moved very fast, according to the state government
representatives that we spoke with. Compared to past events experienced, these funds were
distributed with great efficiency. FEMA’s Public Assistance Program received nearly 450
applications for more than 2,000 projects and totaling more than $413 million. As of April 2017,377 projects had been obligated for over $32 million in federal funding. In NC, mitigation staff
identified 28 projects, totaling more than $1.5 million, for additional mitigation funding throughprograms like FEMA’s Hazard Mitigation 406 program (FEMA 2017). The HMGP implemented
a new streamlined application process which was intended to expedite the submission of
proposals and the subsequent awarding of funds. A range of projects were funded, including
many for infrastructure. In order to bring more funding through the HMGP, additional flood
studies of six river basins were being conducted.
Neither the speed with the FEMA Public Assistance funds nor the efficiency of the HMGP
awards were echoed by the CDBG-DR funds. As of the time of Wave 2, the CDBG-DR funds
had not reached the state for distribution/award to the impacted communities. This was despite
the $198 million in funding having been approved at the federal level in January 2017 (NCDepartment of Public Safety 2017). Once received by the state, 80 % of these funds ($159M)
must be awarded to the four counties with the highest impact; these counties include Robeson.
The remaining 20 % is available by application and competition to the remaining 46 counties in
NC with a disaster declaration. Additional FEMA grant funds totaling $4.5 million were awarded
to NC to match trained case managers with Hurricane Matthew survivors to help them navigate
the long and often complicated recovery process (NC Governor Roy Cooper 2017).
Previously, CDBG-DR funds were not handled by NCEM, but instead were managed throughthe NC Department of Commerce. The new division of responsibility allowed for the realizationof benefits from existing relationships and coordination spurred by the North Carolina Disaster
Recovery Framework. This is best evidenced in the process of decision-making, which involved
all partners, federal, state, and foundation level, coming to the table together. The state
representative’s description of the process included all parties sitting around the table with theirspreadsheets of projects for “horse trading” (i.e., having unofficial discussions to arrive atagreements with benefits for both sides, often used in reference to political negotiations). The
repeated meetings involved discussion and trading of projects, finding ways to collaborate to
fully fund specific projects, and determining which projects would be better funded by one
partner versus another.
State partners included NCEM, Department of Environmental Quality, Department ofAgriculture, among others. Additionally, the foundation partner, Golden LEAF Foundation, wasa key participant in this process. After all, part of the work of Golden LEAF Foundation was touse state funds to fill gaps that could not be addressed by federal funds. Overall, state
representatives describe the funds from the state as having been distributed faster and withgreater flexibility. To this point, the first grants were awarded by the Golden LEAF Foundation81

in April 2017. The state had obligated 93 % of the available funds at the one-year post-event
mark. As of Wave 2, state representatives were projecting a total of more than $375M to be
distributed for Hurricane Matthew. Many agencies, offices, groups, and individuals wereinvolved in the recovery effort, each with a different role, and coordination across the agencies
with funding was identified as particularly critical to the recovery effort.
In order to measure progress in the recovery phase, the state is using a number of approaches,
including tracking projects and grants. A Customer Relationship Management (CRM) software
was being used in an innovative way to track grant funds through the implementation process.
This tool was used for a variety of project types including those funded through CDBG-DR and
HMGP. The state’s resilience office was further tracking the HMGP projects. This monitoring
supported the state’s assessment of the recovery progress over time.
Moving forward, the state’s office of emergency management stated that increasing the portfolio
of affordable housing is the number one issue. Representatives also see this as key to improving
the post-disaster outcomes of communities. To highlight this dedication, a NC Community
Development Initiative was pursued in partnership with UNC-Chapel Hill’s School ofGovernment, Development Finance Initiatives with the aim of rehabilitating public housing units
and creating a land bank to purchase properties that are abandoned or in disrepair (NCCommunity Development Initiative 2017). In light of disagreements between the state andfederal government about who has responsibility for the people living in public housing, the state
made the call that they had to move things forward independent of federal government agencies.
Challenges Remaining for State Government
At the state level, more than 80,000 households had registered with FEMA for some type ofdisaster assistance (NCDPS 2016a). NOAA estimated that Hurricane Matthew cost the state $1.5
billion in property damage (NHC NOAA 2017). Statistics like these represent the challenging
road ahead of the state. As of May 2017, the federal government had authorized $6.1 million of
the more than $900 million originally requested by the state of NC. The NC governor indicatedthat these funds were intended to cover everything from housing and business repairs, elevationof homes, agriculture losses, public facilities repairs, and physical and mental health services(Jarvis 2017). The timing of grants and other funding reaching the state was a major issue. Of
particular concern were releases of the CDBG-DR and Hazard Mitigation Grant Program funds;
according to state government representatives, the delays in distributing these funds represented
a challenge for the state’s recovery path. The data collection and analysis of Wave 2 made clearthat this challenge was being experienced at multiple levels - from the state to the county to thecity to the household.

82

Chapter 5: Conclusions and Next Steps
The field study team conducted Wave 2 as part of the longitudinal community resilience fieldstudy in Lumberton, North Carolina. The Wave 2 data collection represents documentation of
housing data 14 months following the major flooding damage due to Hurricane Matthew in early
October 2016. The business survey, a new addition in Wave 2, measures business recovery using
a variety of indicators alongside housing recovery indicators documented via the housing survey
instrument. The results of the data collection show clear differences across the community inimpacts to businesses and recovery. The public sector interviews and meetings represented an
ongoing effort to understand the broader context of community recovery by taking into accountstate and city government, public schools, and water infrastructure. The variety of recovery
indicators employed in Wave 2 to support the measurement of Lumberton’s progress in recovery
for housing and businesses will be used in Waves 3 and beyond to document the trajectory for
housing and businesses to progress understanding in longitudinal community resilience andrecovery. The information collected in Wave 2 augment findings from Wave 1 to contribute tothe on-going IN-CORE modeling efforts, as well as to NIST research on community resilience.
This chapter provides conclusions related to the housing survey findings, business survey
findings, and public sector interview findings.

5.1 Conclusions for Housing Disruption and Recovery
The housing survey collected data on housing recovery, as well as household dislocation and its
impacts on work and school in Lumberton, NC. The findings of the household survey conductedin Wave 1 indicated that structural damage had a significant impact on population dislocation
and associated social and economic consequences (see van de Lindt et al. 2018). Through the
Wave 2 housing recovery data collection, some respondents shared anecdotally that they wereoriginally dislocated and remained dislocated because of damage, utility disruption, and thetiming of repairs to their homes. The majority of households did not report their dislocation time
being influenced by the four factors we asked households about, namely changes to work,school, other businesses, or timing of resources. This finding does not discredit the importance of
these factors; instead, it raises an important question about the factors that do determine timing
and the decision to return home following a disaster. The findings also raise the question about
whether some households return to their original housing unit despite unrepaired damage. The
relationships that were not observed between dislocation time and return decision with changes
to work, school, other businesses, and timing of resources, may be a result of the limitation ofsurveying households that did return. Perhaps these factors are more influential to thosehouseholds who do not return, and therefore, who were not surveyed as a part of this effort.
As of the time of Wave 2, January 2018, CDBG-DR funds had not been distributed in
Lumberton. As one would expect, it was apparent that funding delays to households had asignificant impact on the ability of Lumberton to begin their recovery. Furthermore, findings
from this study demonstrate the low percentage of at-risk households who carry flood insurance
as well as the low percentage of households that apply for external resources following a disasterevent. Finally, the households that do apply for assistance must be able to wait, in most cases, asubstantial period of time to receive the resources. The high percentage (26 %) of households not
making any progress towards housing recovery 14 months after the flooding was alarming; these83housing units are recorded as abandoned. The status of these housing units and households has
significant implications for community recovery and neighborhood change.
Through survey responses from households with school-aged children and semi-structured
interviews with school representatives (see Chapter 4 of this report and van de Lindt et al. 2018),
it was clear that children and their education were differentially impacted. One school
permanently closed in Lumberton following Hurricane Matthew, requiring the children to beabsorbed into other schools. During the surveys, when parents were asked about their children’s
educational recovery, 14 % of parents were still uncertain because of these changes. Other
parents (10 %) indicated positive changes because their children were better off in their new
school. This can be viewed as a type of unexpected consequence occurring through disaster andrecovery resulting in a positive impact for some, whether or not that is immediately recognized.

5.2 Conclusions for Business Interruption and Recovery
The business survey team collected data on the impacts of Hurricane Matthew and the resulting
interruption; the team also began a longitudinal effort to document recovery. The Lumberton
business survey was successful in that the in-person response rate was quite high (52 %) whencompared to other social science survey collections, giving the team a robust dataset to workwith in drawing the following conclusions. First, the team identified differences in the number ofclosures based on whether the business was in the inundation zone. Businesses in the inundation
area had a higher percentage of closures, with 38 of the 181 (21%) businesses being closed
compared to 20 closures out of the 280 (7%) non-inundated businesses that were drawn in thesample. Future models can statistically test these differences, controlling for other factors,
through logistic regression. In addition, the business team was able to get detailed information on
recovery status for 164 businesses in the sample. Although this report includes cross-tabulations,future work will account for more variables and utilize more sophisticated statistical approaches
to better understand the factors influencing business recovery a year after the event.
The business team also sought to understand how to best measure business recovery using avariety of indicators. The results of the data collection show clear differences in how businesses
respond to different recovery indicators: 24% of businesses indicated that they were still
operating below 100% capacity, whereas close to 34% of businesses are still in survival/responsemode, consider themselves still recovering, or believe they will never recover. Additionally, aneven greater proportion of businesses, close to 45%, report a decrease in revenue since the
Hurricane. Looking at the relationships between these indicators will bring researchers closer tounderstanding business recovery on a deeper level.
For businesses, only a fraction of low-interest loans applied for were approved, which is likely tocause further delay in business recovery. Although many businesses did not apply for or receive
funding, there is still a story to be told in the data. To cover flood events, 68 % of businesses
reported having building insurance, 50 % had contents insurance, and 32 % had interruption
insurance--however, 93 % of businesses experienced interruption, 50 % of businesses reported
contents damage, and only 43 % of businesses reported building damage indicating a coverage
mismatch for this event. Although one might expect additional funding sources to fill the gap,
this was not observed for the businesses in the sample.
84

5.3 Conclusions for Public Sector Impacts and Recovery
The opportunities described in Chapter 4 to interview local and state officials provided insight
into the layers of communication and funding that must be synchronized to ensure disaster relief(and other) funds make their way to the households, businesses, and public sector - the people
and groups that need them. In many cases, federal post-disaster assistance funds were slower
than state and private funds, according to discussions at both the state and local government
levels. However, by January 2018, various sources of funds have been identified and provided bysources including FEMA Public Assistance, the state, and others, and were being distributed
through the Golden LEAF Foundation for key projects, such as public building repair,installation of a berm around the water treatment plant, and flood gates at the VFW Road and
CSX Railroad underpass. The progress is countered by challenges. One third of public housing in
Lumberton was lost as a result of the flooding and the application to replace these buildings had
been denied. As a result, the state has identified affordable housing as one of the most significant
challenges currently facing Lumberton. In addition, the Public Schools of Robeson County arecontinuing to face challenges as students leave the district, annual funding decreases, and money
for repairs and mitigation and preparedness efforts is scarce. With the threat of future hazard
events on the horizon, the lag in recovery increases the concern about the resilience ofLumberton to the next disaster.

5.4 Concluding Methodological Considerations
Several methodological findings also arose from this work that are expected to inform future
waves of data collection in Lumberton, as well as other longitudinal studies of community
resilience and recovery. First, two-person teams should be assigned to in-person data collection
and fieldwork. This provides for improved efficiency and surveyor-respondent rapport byallowing one individual to speak directly with the respondent, observe nonverbal cues, anddemonstrate good listening behaviors while the other individual records notes during the survey
or interview. In the two-person team, one person assumes the speaker/interviewer role, while theother person assumes a recorder role. Allowing one person to speak, and not write
simultaneously, provides for smoother interactions. A two-person team also improves safety
from individual assignments. Second, the flexibility of having two teams work through nearby
neighborhood streets for housing, but not for businesses surveys, was also important for theefficiency of the data collection. The businesses are separated by much greater distance andinvolve substantial driving time between locations. Meanwhile, the housing units are often
located in areas where two teams can effectively work within sight of one another and still sharea car for transportation between sampling areas. Third, a clear final day protocol is necessary forwrapping up surveys to any remaining businesses or housing units in the sample, especially thosebusinesses that had surveys dropped off with them without any way for them to return it. One
option might be to bring pre-stamped envelopes to provide to participants for mailing surveys
back. Another option is to leave an information sheet with a QR code or URL to an online
version of the survey. Either way, this process should be written into the IRB submission(s) inorder to minimize time between survey observations. Relatedly, before departing, the field teamshould confirm that the business phone number in the ReferenceUSA database is correct andobtain the contact information or name of the owner or manager most likely to have knowledge
about the business and Hurricane Matthew, for later follow-up.

85

Lastly, experienced, attentive, and prepared surveyors are key to the increased success rate of thenumber of completed surveys. Many of the households and businesses were under duress andreceived little to no support following Hurricane Matthew. Field team members should thus beprepared to discuss the ways in which the information collected can improve the post-disastersituation for households and business. Empathy should be part of the surveyor’s tools. The
surveyor should clearly communicate that they are cognizant of hardships caused by the event,and that the survey work, and study in general, are intended to contribute to the more equitable
distribution of resources for households and businesses in the future. Having supplemental
materials on hand in all cases (e.g., mental health resources, financial assistance resources) isalso critical to provide to residents or business owners/managers who want or may need thisinformation. This is a small way of giving back to the community. Other forms of giving back to
the community should be discussed and intentionally addressed in future longitudinal studies.

5.5 Next Steps for the Lumberton Longitudinal Field Study
Wave 2 of the Lumberton Field Study represents the next step in the development of alongitudinal dataset; in this case, a dataset that systematically documented recovery trajectories,
infusion of federal, state, and other recovery assistance, and began to augment the types of data
being collected. The second wave was conducted 15 months after the first wave and represents
the first temporal documentation of housing and business recovery for Lumberton. The teamquantified the distribution of the types of assistance (both financial and in-kind) households andbusinesses sought. In addition, in Wave 1 engineering assessments of building damage were
provided by multi-disciplinary teams (see van de Lindt, Peacock, Mitrani-Reiser et al. 2018 fordetails) which were compared with respondent’s reports of damage in the housing survey as partof Wave 2. Although this may result in some discrepancies, multiple measurements of damage
provide the study team the ability to ascertain the full damage for housing units as well as tobetter quantify the differences between the two types of data. Both of these outcomes are ofvalue from a disaster metrology perspective.
One of the initial objectives from Wave 1 of the Lumberton longitudinal field study was toadvance the area of field study metrology with a focus on community resilience. In Wave 2, thestudy was expanded from households to include business surveys to evaluate the current status ofrecovery for business within Lumberton. The ability to systematically document inflow ofdisaster relief funds and observed recovery at the household and individual business level overtime will provide a seminal dataset for community resilience researchers. A number ofmethodological lessons and recommended improvements are also being identified and outlined
in a subsequent report of field study best practices to ensure benefit to those engaging in fieldstudies and to communities like Lumberton.
As the Lumberton longitudinal field study continues and the dataset is built over time, it isanticipated that the field study will provide major contributions to efforts to model community
resilience and recovery. In order to accomplish the longer-term objectives set out by the team,these next steps are recommended:
1. Both the housing and business survey collections should be continued every 12-16
months with improvements and appropriate temporally-based changes made as needed.
86The surveys should not, however, be lengthened, in an effort to maintain response rates
and minimize population burden and survey fatigue.
2. Temporal analyses should be conducted in order to compare results across time points,
e.g., compare dislocation across time, influence of level of damage (Wave 1) on recovery
status of housing (Wave 2 and subsequent datasets).
3. A comprehensive analysis should be conducted to ensure that the surveys align with whatis needed to model community resilience in IN-CORE and IN-CORE validation. Thisshould include, but not be limited to, housing recovery, business recovery, mitigation andbehavioral actions, but also validation of the full architecture as the Lumberton study
progresses.
4. Ongoing analysis should be conducted to ensure that the field study continues to alignwith what is needed for NIST measurement and modeling, and improvements tostandardized field study protocols. This includes the use of data in the development of theAlternatives for Resilient Communities (ARC) model, a web-based tool to assist incommunity resilience planning, for recovery measurement to inform the Tracking
Community Resilience (TraCR) Methodology, and accounting for disaster losses with afocus on major indirect losses, such as business interruption, and distributional effects.
5. Housing and business survey instruments and collected data should be published andreceive a Digital Object Identifier (DOI). Care should be taken to ensure that thosecontributing to the survey development receive proper credit for their work. Likewise, thefield of disaster research should benefit from instruments that support the collection ofstandardized data.
6. A Lumberton Field Study workshop, held independently of the field study itself, shouldbe planned and executed. This workshop can help satisfy two pressing needs: (a) the firsthalf of the workshop should include discussion on how IN-CORE models can bevalidated with data from Lumberton, data storage and sharing protocols including theneed to protect identifiable information, and publication of survey instruments andsupporting documentation for DOI’s related to those publications; (b) the second half of
the workshop should focus on developing the white papers associated with each of these
topics to ensure progress is made and lessons learned are shared beyond the study team.

87

References
Abramson, D. M., Stehling-Ariza, T., Park, Y. S, Walsh, L., and Culp, D. (2010). Measuring
individual disaster recovery: A socio-ecological framework. Disaster Medicine and Public
Health Preparedness, 4, S46-S54.
[ACS] American Community Survey. (2020). “2016: ACS 5-Year Estimates Data Profiles”.
United States Census Bureau.
American Water Works Association/ Water/Wastewater Agency Response Network. (2018).
Hurricanes Harvey and Irma After-Action Report.
(https://www.awwa.org/Portals/0/AWWA/Government/HarveyIrmaAARFINAL.pdf?ver=20
18-05-17-153257-720)
America's Water Infrastructure Act of 2018, S.3021 — 115th Congress (2017-2018). Section
1433, Public Law No: 115-270.
Bigelow, S. (2018). It’s official: West Lumberton to close. Robesonian.
(https://www.robesonian.com/news/112078/its-official-west-lumberton-to-close)
Colman, Zack and Daniel Cusick. (2018). 2 towns, 2 storms and America's imperiled poor. E&E
News reporters Climatewire. (https://www.eenews.net/stories/1060100111/)
Corey, C. M., and Deitch, E. A. (2011). Factors Affecting Business Recovery Immediately after
Hurricane Katrina. Journal of Contingencies and Crisis Management, 19(3), 169-181.
doi:10.1111/j.1468-5973.2011.00642.x
Dahlhamer, J. M., and Tierney, K. J. (1996). Winners and losers: predicting business disasterrecovery outcomes following the Northridge earthquake.
Dahlhamer, J. M., & Tierney, K. J. (1998). Rebounding from disruptive events: Businessrecovery following the Northridge earthquake. Sociological Spectrum, 18(2), 121-141.
Department of Energy (2018). Energy Resilience Solutions for the Puerto Rico Grid, Final
ReportDietch, E. A., and Corey, C. M. (2011). Predicting long-term business recovery four years after
Hurricane Katrina. Management Research Review, 34(3), 311-324. doi:
10.1108/01409171111116321[FEMA] Federal Emergency Management Agency. 2016, 2016 PA Program and Policy Guide(PAPPG) [V1.0], (https://www.fema.gov/media-library/assets/documents/111781)

88

[FEMA] Federal Emergency Management Agency. 2017. Six Months Following Hurricane
Matthew. FEMA News Release. (https://www.fema.gov/news-release/2017/04/06/sixmonths-following-hurricane-matthew-government-partners-volunteers-work)[FEMA] Federal Emergency Management Agency. 2018a. Hazard Mitigation Assistance Grants.
(https://www.fema.gov/hazard-mitigation-grant-program)[FEMA] Federal Emergency Management Agency. 2018b. Codes and Standards – 50 Percent
Rule, Appeal Brief. (https://www.fema.gov/appeal/335606?appeal_page=analysis#_ftn33)
Fothergill, A., and Peek, L. A. (2004). Poverty and disasters in the United States: A review ofrecent sociological findings. Natural Hazards, 32(1), 89-110.
Fothergill, A. and L. Peek. (2015). Children of Katrina. Austin: University of Texas Press.
Furman Center 2017. Housing in the U.S. Floodplains. Data Brief, May 2017.
(http://furmancenter.org/files/NYUFurmanCenter_HousingInTheFloodplain_May2017.pdf)
Gerlach (2017). Hurricane Matthew: The Aftermath. Opinion-Editorial, The News and Observer.
October 06, 2017 10:47 AM. (https://www.newsobserver.com/opinion/oped/article177468181.html)
Golden LEAF Foundation. (2018). About Golden LEAF Foundation. Retrieved from(https://www.goldenleaf.org/about/)[HUD] Housing and Urban Development. (2018). Picture of Subsidized Households 2018.
(https://www.huduser.gov/portal/datasets/assthsg.html)
Hunter, T. C. (2017). West Lumberton pupils to stay put. Robesonian.
(https://www.robesonian.com/news/97923/west-lumberton-pupils-to-stay-put)
InfoGroup Inc. (2016). U.S. Historical Businesses: All businesses in Lumberton, NC in year
2016 [Data file]. Retrieved from http://referenceusa.com/UsHistoricalBusiness
Jarvis, Craig. (2017). Cooper ‘shocked, disappointed’ in federal storm recovery funds from
Trump administration. The News and Observer, May 11, 2017.
(https://www.newsobserver.com/news/politics-government/statepolitics/article149676019.html)
Koenig, T.A., Bruce, J.L., O’Connor, J.E., McGee, B.D., Holmes, R.R., Jr., Hollins, Ryan,
Forbes, B.T., Kohn, M.S., Schellekens, M.F., Martin, Z.W., and Peppler, M.C. (2016).
Identifying and preserving high-water mark data: U.S. Geological Survey Techniques andMethods, book 3, chap. A24, 47 p. (http://dx.doi.org/10.3133/tm3A24)
Lam, N. S. N., Arenas, H., Pace, K., LeSage, J., and Campanella, R. (2012). Predictors ofBusiness Return in New Orleans after Hurricane Katrina. PLoS One, 7(10).
doi:10.1371/journal.pone.0047935
89

Lesage, J., Pace, R. K., Lam, N., Liu, X., and Campanella, R. (2011). Do what the neighbours
do: Reopening businesses after Hurricane Katrina. Significance, 8(4), 160-163.
doi:10.1111/j.1740-9713.2011.00520.x
Marshall, M. I., Niehm, L. S., Sydnor, S. B., and Schrank, H. L. (2015). Predicting small
business demise after a natural disaster: an analysis of pre-existing conditions. Natural
Hazards, 79(1), 331-354. doi:10.1007/s11069-015-1845-0
McDonald, T. M., Florax, R., and Marshall, M. I. (2014). Informal and Formal Financial
Resources and Small Business Resilience to Disasters. St. Louis: Federal Reserve Bank of St
Louis.
Merdjanoff, A. (2015). Weathering the storm: the long-term effects of Hurricane Katrina on
mental health, mobility, and recovery. Ph.D. dissertation, Rutgers University.
Mitrani-Reiser, J. (2007). “An ounce of prevention: Probabilistic loss estimation for
performance-based earthquake engineering.” Ph.D. thesis, California Institute ofTechnology, Pasadena, CA.
[NHC NOAA] National Hurricane Center, National Oceanic and Atmospheric Administration.
(2017). Hurricane Matthew, National Hurricane Center Tropical Cyclone Report. April 7,
2017. (https://www.nhc.noaa.gov/data/tcr/AL142016_Matthew.pdf)
Nejat, A. and Damnjanovic, I. (2012). Agent-based modeling of behavioral housing recovery
following disasters. Computer-Aided Civil and Infrastructure Engineering, 27, 748-763.
[NOAA] National Oceanic and Atmospheric Association. (2018). National Centers forEnvironmental Information’s U.S. Billion-Dollar Weather and Climate Disasters.
(https://www.ncdc.noaa.gov/billions/)
NC Community Development Initiative. 2017. “Second Emergency Management Loan to Create
Affordable Housing in Lumberton”, October 11, 2017.
(https://ncinitiative.org/2017/10/11/second-emergency-management-loan-to-createaffordable-housing-in-lumberton/)[NCDPS] NC Department of Public Safety. (2017) Governor Cooper announces $198 million
grant for Hurricane Matthew recovery: Funds will help survivors with housing, rebuilding,jobs in hardest hit counties. Press Release, January 26, 2017.
(https://www.ncdps.gov/governor-cooper-announces-198-million-grant-hurricane-matthewrecovery)
NC Governor Roy Cooper (2017) Gov. Cooper Announces $4.5M Federal Grant to Help
Matthew Survivors. Press Release, January 18, 2017. (https://governor.nc.gov/news/govcooper-announces-45m-federal-grant-help-matthew-survivors)

90

NC State Building Code Council. (2014). 2015 North Carolina Existing Building Code.
(https://www.ncosfm.gov/2015-north-carolina-existing-building-code-changes-only-2012iebc-140401-rrc-140522-icc/open)
Olsen, M. J., Barbosa, A., Burns, P., Kashani, A., Wang, H., Veletzos, M., Chen, Z., Roe, G.,
and Tabrizi, K. (2016). Assessing, Coding, and Marking of Highway Structures in
Emergency Situations, Volume 2: Assessment Process Manual (No. Project NCHRP 833).
(https://doi.org/10.17226/24610)
Peacock, W. Rosenheim, N. Gu, D. Van Zandt, S. Peek, L. Dillard, M. Tobin, J. Hamideh, S.
(2020). "Household Survey Instrument, November 26, 2016: Wave 1", in A LongitudinalCommunity Resilience Focused Technical Investigation of the Lumberton, North Carolina
Flood of 2016. DesignSafe-CI. (https://doi.org/10.17603/ds2-pmt9-1s33)
Quillin, Martha. (2016). A month after Hurricane Matthew, many in Lumberton despair of evergetting back to normal. News and Observer. Updated November 16, 2016 10:07 AM.
(https://www.newsobserver.com/news/state/article114997813.html)
Quillin, Martha. (2017). Sleeping on floors and in boarded up hotels. 7 months after Matthew,
renters still mired in misery. News and Observer. Updated April 10, 2018 03:38 PM.
(https://www.newsobserver.com/news/state/article150367937.html)
Redlener, I and Schlegelmilch, J (2017, October 18) Launching the Children’s Disaster
Resiliency and Recovery program (CDRR). National Center for Disaster Preparedness.
(http://ncdp.columbia.edu/ncdp-perspectives/launching-childrens-disaster-resiliencyrecovery-program-cdrr/)
Salkind, Neil J. (2010). “Triangulation”, In Encyclopedia of Research Design. Thousand Oaks,
CA: Sage Publishing. (http://dx.doi.org/10.4135/9781412961288)
Sutley, E.J. and Hamideh, S. (2017). An Interdisciplinary System Dynamics Model for PostDisaster Housing Recovery. Sustainable and Resilient Infrastructure, DOI
10.1080/23789689.2017.1364561.
Sutley, E.J., and Hamideh, S. (2020). “Postdisaster Housing Stages: A Markov Chain Approachto Model Sequences and Duration Based on Social Vulnerability.” Risk Analysis.
(https://doi.org/10.1111/risa.13576)[USACE] US Army Corps of Engineers. (2012). Levee Certification vs. Accreditation.
(https://www.mvk.usace.army.mil/Portals/58/docs/LSAC/LeveeCertification.pdf)
US Census Bureau (2017). “Population and Housing Unit Estimates.” United States Census
Bureau. (https://www.census.gov/programs-surveys/popest.html)
van de Lindt, J.W., W. G. Peacock, J. Mitrani-Reiser, N. Rosenheim, D. Deniz, M. Dillard, T.
Tomiczek, M. Koliou, A. Graettinger, S. Crawford, K. Harrison, A. Barbosa, J. Tobin, J.
Helgeson, L. Peek, M. Memari, E. Sutley, D. Gu, S. Cauffman, and J. Fung. (2018).
91

Community Resilience-Focused Technical Investigation of the 2016 Lumberton, North
Carolina Flood: Multi-Disciplinary Approach. van de Lindt, J.W., W. G. Peacock, and J.
Mitrani-Reiser (Eds). National Institute of Standards and Technology (NIST SP 1230).
(https://doi.org/10.6028.NIST.SP.1230)
Watson, M., Xiao, Y., Helgeson, J., & Dillard, M. (2020). Importance of Households in Business
Disaster Recovery. Natural Hazards Review, 21(4), 05020008.
Webb, G. R., Tierney, K. J., & Dahlhamer, J. M. (2002). Predicting long-term business recovery
from disaster: a comparison of the Loma Prieta earthquake and Hurricane Andrew. Global
Environmental Change Part B: Environmental Hazards, 4(2), 45-58.
Whittaker, S. (2017). Mobile Water Units Help Recovery Efforts in Lumberton, N.C.
(https://www.waterworld.com/articles/print/volume-33/issue-6/departments/casestudies/mobile-water-units-help-recovery-efforts-in-lumberton-n-c.html)
Xiao, Y., and Van Zandt, S. (2012). Building Community Resiliency: Spatial Links between
Household and Business Post-disaster Return. Urban Studies, 49(11), 2523-2542.
doi:10.1177/0042098011428178

92

Appendix 1A: Study Information Sheets
INFORMATION SHEET FOR HOUSEHOLDS

Hurricane Matthew Community Recovery Study for Lumberton, NC
Center for Risk-Based Community Resilience Planning
A U.S. National Institute of Standards and Technology-funded Center of Excellence_______________________________________________________________________The Center for Risk-Based Community Resilience Planning is based at Colorado State University in Fort
Collins, Colorado, and includes collaborations with researchers from universities across the United States.
The National Institute of Standards and Technology’s Community Resilience Program is based in
Gaithersburg, Maryland and includes engineers, economists, and sociologists. Collectively, we areworking to understand what makes a community “resilient” – or able to bounce back – in the face ofdisaster.
Our research in Lumberton focuses on community recovery following the flooding that occurred due toHurricane Matthew in early October 2016. We are returning to Lumberton this year to follow up our
initial study in November 2016. We will be collecting information from households like yours to gain
knowledge on the impacts and the recovery from Hurricane Matthew and the flooding. We hope to learn
from your experiences to help communities better prepare for similar events in the future.
This research is part of a five-year project that will be carried out by experts from engineering, the social
sciences, economics, and many other disciplines. This community was selected as one of our five research
locations around the country that we hope to learn from. Our field team will be in Lumberton fromJanuary 19 through 29, 2017.
Because NIST is part of the federal government, this research was reviewed through a special process.
This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the
Office of Management and Budget (OMB). Notwithstanding any other provisions of the law, no person isrequired to respond to, nor shall any person be subject to a penalty for failure to comply with, a collectionof information subject to the requirements of the PRA unless that collection of information displays acurrently valid OMB control number. For this collection, the OMB Control number is:0693-0078 with an
expiration date: July 31, 2019.
Public reporting burden for this collection is estimated to be 10 minutes per survey, including the time forreviewing instructions, searching existing data sources, gathering and maintaining the data needed andcompleting and reviewing the collection of information. Send comments regarding this burden estimate or
any aspect of this collection of information, including suggestions for reducing this burden, to the
National Institute of Standards and Technology, Attn: Dr. Jennifer Helgeson, NIST, 100 Bureau Drive,
MS 8603, Gaithersburg, MD 20899-1710, telephone 301-975-6133, or via
email:jennifer.helgeson@nist.gov
If you have more general questions about the project or the Center for Risk-Based Community Resilience
Planning, please contact: Dr. John van de Lindt at 970-218-4076 or via email: t jwv@engr.colostate.edu.

A-1

INFORMATION SHEET FOR BUSINESSES

Hurricane Matthew Community Recovery Study for Lumberton, NC
Center for Risk-Based Community Resilience Planning
A U.S. National Institute of Standards and Technology-funded Center of Excellence_______________________________________________________________________The Center for Risk-Based Community Resilience Planning is based at Colorado State University in Fort
Collins, Colorado, and includes collaborations with researchers from universities across the United States.
The National Institute of Standards and Technology’s Community Resilience Program is based in
Gaithersburg, Maryland and includes engineers, economists, and sociologists. Collectively, we areworking to understand what makes a community “resilient” – or able to bounce back – in the face ofdisaster.
Our research in Lumberton focuses on community recovery following the flooding that occurred due toHurricane Matthew in early October 2016. We are returning to Lumberton this year to follow up our
initial study in November 2016. We will be collecting information from businesses like yours to gain
knowledge on the impacts and the recovery from Hurricane Matthew and the flooding. We hope to learn
from your experiences to help communities better prepare for similar events in the future.
This research is part of a five-year project that will be carried out by experts from engineering, the social
sciences, economics, and many other disciplines. This community was selected as one of our five research
locations around the country that we hope to learn from. Our field team will be in Lumberton fromJanuary 19 through 29, 2017.
Because NIST is part of the federal government, this research was reviewed through a special process.
This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the
Office of Management and Budget (OMB). Notwithstanding any other provisions of the law, no person
is required to respond to, nor shall any person be subject to a penalty for failure to comply with, acollection of information subject to the requirements of the PRA unless that collection of information
displays a currently valid OMB control number. For this collection, the OMB Control number is:06930078 with an expiration date: July 31, 2019.
Public reporting burden for this collection is estimated to be 15 minutes per survey, including the time forreviewing instructions, searching existing data sources, gathering and maintaining the data needed andcompleting and reviewing the collection of information. Send comments regarding this burden estimate or
any aspect of this collection of information, including suggestions for reducing this burden, to the
National Institute of Standards and Technology, Attn: Dr. Jennifer Helgeson, NIST, 100 Bureau Drive,
MS 8603, Gaithersburg, MD 20899-1710, telephone 301-975-6133, or via
email:jennifer.helgeson@nist.gov
If you have more general questions about the project or the Center for Risk-Based Community Resilience
Planning, please contact: Dr. John van de Lindt at 970-218-4076 or via email: jwv@engr.colostate.edu.

A-2

Appendix 1B: Mental Health Resources
MENTAL HEALTH RESOURCES FOR LUMBERTON
Mental Health Resources
Robeson County
Should you need additional assistance, the following local resources are available to provide
timely services.
Phone Access:Eastpointe Access Center is available 24 hours a day, 7 days a week. Customer Service
Specialists will assist you to find a crisis provider that is well-matched with your needs. Your
local number is: 800-913-6109 or for TTY 888-819-5112
If you already have a service provider, call them first. Providers who know you are usually best
prepared to assist you in a crisis.
In-home:Crisis situations are often best resolved at home. Mobile Crisis Teams are available 24 hours a
day in all counties. Professional counselors will speak with you and your family during a visit.
They have an average response time of 2 hours. This service is provided by:
Monarch910-618-5606
Crisis Center:
Many counties have a specialized crisis center where you can walk in for a crisis assessment andreferrals to additional services. Appointments are not needed. The crisis center in your county isprovided by:
Monarch207 W. 29th St, Lumberton NC
910-618-5606
Monday - Friday, 8:00 a.m. - 5:00 p.m.
Crisis Services for Robeson County are managed by: Eastpointe

A-3

Appendix 2A: Housing Survey
One-Year Post-Hurricane Matthew Field Study in Lumberton, North Carolina

Housing/Household Recovery Survey
OMB CONTROL NO. 0693-0078 Expiration date: 07/31/2019

Surveyor(s):_____________________________________ Date:____________________
Building ID: _____________________________________Unit Address:______________
Building Type: 1, Single family 2, Multi-family, # housing units _______ 3, Mobile home 4, Other, __________
Comments:_________________________________________________________________________________________
Result/Completion
Codes
Housing Unit
(HU)
Occupancy
status:1. Completed interview
2. Ineligible, no adult or eligible person to answer questions
3. Bad address, could not locate HU
4. Incomplete/partial
5. Not occupied residence, abandoned; home destroyed
6. Ineligible, structure not a residence
7. No Answer or response, but evidence or confirmed occupied
8. No access, gated community, fence preventing entry

YES: household presentinterviewed or attempted

YES, evidence of current
habitation

DK: Indeterminate/
uncertain

NO: not occupied, appears
abandoned

YES, occupied confirmedby neighbor

YES, occupied, confirmed
by management

NO: not occupied, underrepair/reconstruction.

NO, damaged and nothabitable

If interview not possible but neighbors, apartment managers, or others can provide information,
record here:
If NO: Do you know where the former
Mark type of
Was the HU occupied at time of HM?
household lives now?
informant:
YES, #HH members_______NO
DK
LumbertonNC
Out of State
DK
Neighbor
Is household still living there?
If NO: Will former HH return at any point?
Manager
YES
NO
DK
YES
NO
DK
Other: (specify)________________ Did household leave because of the flooding?
YES
NO DK
[The following questions are to be answered upon consent of a HH member over 18 years of age.]
1. Was your household living in this home at the time of Hurricane Matthew (October 2016)?
Yes
No
2. How many people are in your household?
18)_______

Adults (over 18)_______ Children (under[If NO to Q1, ask Q3 – Q7, Q27-Q30 and then close survey. If YES to Q1, ask Q2 and then skip to Q8.]
3. When did you move to this housing unit? DD/MM/YY____________________________

A-4

4. Do you know where the former household lives now?
Lumberton
North Carolina
Out of State
DK
5. Was your household living in Lumberton at the time of Hurricane Matthew?
YesNo
6. Was this house damaged from flooding when you moved in?

Yes

No

DK

a. If YES, how would you classify the damage level? [hand respondent DS card]
Minor
Moderate
SevereCompleteb. If YES, how long until it was fully repaired?_________ days/weeks/months
repaired
7. Do you mind if someone stops by to follow up with more questions later this week?
No

Still notYes

[Skip to Q27. If YES to Q1, continue to Q8.] The first set of questions are about initial damage andrepair, and any assistance from insurance, FEMA, or other sources you may have received to help withyour housing recovery.
8. Was this house damaged from the flooding?

Yes

No

DK

a. If YES, how would you classify the damage level? [hand respondent DS card]
Minor

Moderate

SevereCompleteb. If YES, how long until it was fully repaired?_________ days/weeks/months
repaired
9. Did your household OWN or RENT this house before
the flooding?

Still not

Own

Rent

Other, specify___________

a. If RENT, did you have renter’s insurance?

Yes

No

DK

b. If OWN, did you have flood insurance?

Yes

No

DK

c. If OWN, did you have homeowner’s insurance?

Yes

No

DK

d. If YES to INSURANCE, did you receive a payout for damages from the flooding?
[Circle all that apply]
Yes, from renters ins.
Yes, from flood ins.
Yes, from homeowner’s ins.
No

DK

e. If YES, when did you receive your insurance payout(s)? (Date mm/yr)____________________[Homeowners continue to next question; Renters skip to Q16.] Now I am going to ask you about
seven different types of assistance to learn what resources were available to community members.
f. How many of your repairs were covered by the insurance payout(s)?
Very little Some Almost all/All

A-5

g. Did you have a mortgage?

Yes

No

For each of the following types of disaster recovery
assistance, did you apply for, and/or receive the following:
10. Home repair funds from FEMA (IHP)
11. SBA (Small Business Administration) loans
12. Home repair grant (HUD/CDBG-DR) from the government
13. Financial assistance from any other organization that was
not governmental?
14. Clean up or repair help from any other organization thatwas not governmental?
15. Financial assistance from friends or family

DK
Apply?
(Y or N)

Receive?
(Y or N)

If received, how longafter the flood? wks/mo.

Yes

No

DK

Very little

Some

Almostall/all

Yes

No

DK

N/A

16. If you received any of the above types of assistance and/or insurance,
was the total amount you received enough to pay for repair/replacementof all of the physical damages to your home and contents?
a. If NO, how many of your repairs/replacement were covered?
b. If NO, did you and your household personally pay for the rest?

The next set of questions are intended to capture the impacts of Hurricane Matthew and the flooding
on your household.
17. How long were you and your household dislocated from your home because of the flood?
_________d/wk/mo
18. Did the timing of your insurance payout or other financial assistance cause a delay in yourhousehold returning to your home?
Yes
No
DK
19. In the past 14 months, did you or household members miss work due to housing issues caused by
the flooding?
[housing issues are any problems with your house that started with Hurricane Matthew and/or theflooding]
Yes
No
DK
a. If YES, what was the greatest number of days/weeks of work missed by household members:_______d/wk
b. If YES, where does the person who missed the greatest number of days/weeks work?_______
20. Did a closure or change in your place of work cause a delay in yourhousehold returning to your home?

Yes

No

DK

A-6

21. Did a closure or change in your child/ren’s school cause a delay inyour household returning to your home?

Yes

No

DK

22. Did closure of businesses, such as day care or grocery stores, cause
a delay in your household returning to your home?
Yes

No

DK

23. Do you and your household have the same access to school, work,
grocery stores, and other essential needs in this home as you did
before the flooding?

Yes

No

DK

24. Do you and your household plan to move to a different housing
unit within the next year because of any issue that unfolded as aresult of Hurricane Matthew?

Yes

No

DK

[See Q2, if 0 children in HH, skip to Q27] Next, I want to ask two questions about the experience ofthe children in your household following Hurricane Matthew.
25. Was your child/ren enrolled in school at the time of the flooding? Yes
No[If NO,skip to Q27.]
26. When thinking about your child/ren’s educational recovery following Hurricane Matthew, would
you say that your child/ren’s educational situation is…
a. Better than it was before the flooding
b. Back to where it was before the floodingc. Worse than before the flooding
d. Uncertain, things are still changing for you child/rene. Other (Please specify)_____________________________________
Finally, I have four questions about your household in general.
27. When considering all of the members inyour household, what is the highest numberof years of schooling completed?
28. While we often ask about each memberof a household, in general, when considering
your household how would you characterize
its racial makeup?
29. Are members of your household ofHispanic or Latino origin?
30. Finally, I don’t want to know the exact
amount, but can you identify the letter
associated with the category that best
captures your household’s combined annual
income? (hand respondent the income card)

Enter number of years _______
and/or indicate type of diploma or
degreeHigh School
Associate’s degree
Bachelor’s degree
Master’s deg. or higher

1) White
2) Black or African American3) American Indian or Native American4) Asian (Asian Indian, Chinese, Korean)5) Native Hawaiian or other Pacific Islander
6) More than one (specify - codes): ______
7) Other/Mixed (Specify):________________

1) No, none of Hispanic or Latino origin
2) Yes, Hispanic or Latino
A. $1 to $3,999
B. $4,000 to $5,999
C. $6,000 to $7,999
D. $8,000 to $9,999
E. $10,000 to $11,999

F. $12,000 to $14,999
G. $15,000 to $19,999
H. $20,000 to $24,999
I. $25,000 to $29,999
J. $30,000 to $39,999

K. $40,000 to $49,999
L. $50,000 to $74,999
M. $75,000 to $99,999
N. $100,000 to $149,999
O. $150,000+

A-7

Thank you for your time!
This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the
Office of Management and Budget (OMB). Notwithstanding any other provisions of the law, no person isrequired to respond to, nor shall any person be subject to a penalty for failure to comply with, acollection of information subject to the requirements of the PRA unless that collection of information
displays a currently valid OMB control number. For this collection, the OMB Control number is:06930078 with an expiration date: July 31, 2019. Public reporting burden for this collection is estimated to be
15 minutes per survey, including the time for reviewing instructions, searching existing data sources,
gathering and maintaining the data needed and completing and reviewing the collection of information.
Send comments regarding this burden estimate or any aspect of this collection of information, including
suggestions for reducing this burden, to the National Institute of Standards and Technology, Attn: Dr.
Jennifer Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710, telephone 301-9756133, or via email:jennifer.helgeson@nist.gov.

A-8

Appendix 2B: Residential Damage States and Income Levels
Lumberton Field Study
Housing Survey

Overall damage description for the residential structures
DS Level

Description

0; NoneNo damage; water enters crawlspace or touches foundation (crawlspace or slab on
grade). No contact to electrical or plumbing, etc. in crawlspace. No contact withfloor joists. No sewer backup into living area.

1; Minor

Water touches floor joists up to minor water enters house; damage to carpets, pads,
baseboards, flooring. Approximately 1” in house but no drywall damage. Could havesome mold on subfloor above crawlspace. Could have minor sewer backup and/or
minor mold issues.

2; Moderate

Water level approximately 2 feet with associated drywall damage and electricaldamage, water heater and furnace and other major equipment on first floor
damaged. Lower bathroom and kitchen cabinets damaged. Doors or windows mayneed replacement. Could have major sewer backup and /or major mold issues.

3; Severe

Water level 2 feet to 8 feet; substantial drywall damage, electrical panel destroyed,bathroom/kitchen cabinets and appliances damaged; lighting fixtures on walls
destroyed; ceiling lighting may be ok. Studs reusable; some may be damaged. Could
have major sewer backup and/or major mold issues.

4; Complete

Significant structural damage present; all drywall, appliances, cabinets etc.
destroyed. Could be floated off foundation. Building must be demolished or
potentially replaced.

A-9

We do not want to know the exact amount, but please identify the letter associated with the categorythat best captures your household’s combined income? Annual and Monthly income categories areprovided.
Combined Household Income Categories - Annual
A. $1 to $3,999

F. $12,000 to $14,999

K. $40,000 to $49,999

B. $4,000 to $5,999

G. $15,000 to $19,999

L. $50,000 to $74,999

C. $6,000 to $7,999

H. $20,000 to $24,999

M. $75,000 to $99,999

D. $8,000 to $9,999

I. $25,000 to $29,999

N. $100,000 to$149,999

E. $10,000 to $11,999

J. $30,000 to $39,999

O. $150,000+

Combined Household Income Categories - Monthly
A. $1 to $333

F. $1,000 to $1,249

K. $3,334 to $4,166

B. $334 to $499

G. $1,250 to $1,666

L. $4,167 to $6,249

C. $500 to $666

H. $1,667 to $2,803

M. $6,250 to $8,333

D. $667 to $833

I. $2,804 to $2,499

N. $8,334 to $12,499

E. $834 to $999

J. $2,500 to $3,333

O. $12,500+

A-10

Appendix 2C: Housing Consent Script
One-Year Post-Hurricane Matthew Field Study in Lumberton, North
Carolina
Housing/Household Recovery Survey
Consent Script
OMB CONTROL NO. 0693-0078 Expiration date: 07/31/2019
Hello, my name is (interviewer name) and I am a researcher from (name of university or
National Institute of Standards and Technology) in the (department name/Engineering
Laboratory). We are conducting a research study in Lumberton on community recovery
following the flooding that occurred due to Hurricane Matthew in early October 2016. We are
returning to Lumberton this year to follow up our initial study in November 2016. Someone fromour team may have spoken with you or a household member last year when we conducted our
first study. We would like to speak with you about how this event has continued to affect yourhousehold. In particular, we are interested in learning about the process of recovering from theflooding.
This study is part of a larger project led by Center of Excellence for Risk-Based Community
Resilience Planning at Colorado State University. This project is led by Drs. John van deLindt and Bruce Ellingwood, both Professors from the Civil and Environmental Engineering
Department and is funded by the National Institute of Standards and Technology (NIST).
We would like to ask you some brief survey questions about your household’s experience after
the flood as well as some details about your home during this time. Participation will takeapproximately ten minutes, depending on the experience of your household with Hurricane
Matthew. Your participation is voluntary. If you decide to participate in the study, you may
withdraw your consent and stop participation at any time without penalty.
We will be collecting information about the damage to your home, the process of making repairs,
and how the flood disrupted your household’s living arrangements and daily routines, such as
going to work and school. When we report and share our findings, we will combine the datafrom all participants into summary statistics and tables so no unique individual or household canbe identified. There are NO KNOWN RISKS or direct benefits to you. We hope to gain more
knowledge on how you and others were affected by Hurricane Matthew and the flooding so that
we can learn from your experiences to help communities better prepare for similar events in the
future.
So again, we would like to speak with an adult member of the household that was here at thetime of Hurricane Matthew and the flooding OR someone who knows about what
happened to the household around that time. Would that person be you and would you be
willing to participate?
This collection of information contains Paperwork Reduction Act (PRA) requirements approvedby the Office of Management and Budget (OMB). Notwithstanding any other provisions of theA-11

law, no person is required to respond to, nor shall any person be subject to a penalty for failure
to comply with, a collection of information subject to the requirements of the PRA unless thatcollection of information displays a currently valid OMB control number. For this collection, theOMB Control number is:0693-0078 with an expiration date: July 31, 2019. Public reporting
burden for this collection is estimated to be 10 minutes per survey, including the time forreviewing instructions, searching existing data sources, gathering and maintaining the dataneeded and completing and reviewing the collection of information. Send comments regarding
this burden estimate or any aspect of this collection of information, including suggestions for
reducing this burden, to the National Institute of Standards and Technology, Attn: Dr. Jennifer
Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710, telephone 301975-6133, or via email: jennifer.helgeson@nist.gov.
Privacy Act Statement
Authority: The Paperwork Reduction Act of 1980 (Pub. L. No. 96-511, 94 Stat. 2812, codified
at 44 U.S.C. §§ 3501–3521).
Purpose: The National Institute for Standards and Technology (NIST) conducts Community
Resilience research and surveys through the Generic Paperwork Reduction Act Clearance, OMB
CONTROL NO. 0693-0078 Expiration date: 07/31/2019.
Routine Uses: NIST will use this information to conduct a systematic process evaluation of the
NIST Community Resilience Planning Guide implementation. This is not a Privacy Act system
of Records, therefore there is no System of Records Notice associated with this collection.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are
indicating your voluntary consent for NIST to use of the information you submit for the purpose
stated.

A-12

Appendix 2D: Description of External Recovery Funding Programs
FEMA’s Individuals and Households Program (IHP) provides financial and direct services toeligible individuals and households affected by a disaster who have uninsured or underinsured
necessary expenses and serious needs. IHP is not a substitute for insurance and cannot
compensate for all losses caused by a disaster; it is intended to meet the survivor’s basic needs
and supplement disaster recovery efforts. IHP: Individuals and Households Program.
SBA (Small Business Administration) loans
SBA provides low-interest disaster loans to businesses of all sizes, private non-profit
organizations, homeowners, and renters. SBA disaster loans can be used to repair or replace thefollowing items damaged or destroyed in a declared disaster: real estate, personal property,
machinery and equipment, and inventory and business assets. (Note: Hurricane Matthew inNorth Carolina was not listed under the SBA current disaster declarations. This could imply thewindow for SBA applications is closed. Only two 2017 North Carolina events are listed under
SBA and presidential declarations on SBA website)
CDBG-DR: Community Development Block Grant-Disaster Recovery
From the State of North Carolina CDBG-DR Action Plan CDBG-DR Grants under Public Law
114-223/254 Published April 21, 2017, pages 50-55
For Surveyors’ information: “The Department of Housing and Urban Development (HUD)
appropriated $198,553,000 in Community Development Block Grant Disaster Recovery (CDBGDR) funding to the State of North Carolina, using the best available data to identify and calculate
unmet needs for disaster relief, long-term recovery, restoration of infrastructure, and housing andeconomic revitalization. In addition, 80% of the total funds, or $158,842,400, will go to the mostimpacted areas: Robeson (where Lumberton is), Cumberland, Edgecombe, and Wayne counties.
Within these counties, the following cities were most impacted: Fair Bluff; Fayetteville;
Cumberland; Princeville; Edgecombe; Lumberton; Robeson; Goldsboro; and Wayne. These
funds are to be used in order to satisfy a portion of unmet need that still remains after otherfederal assistance, such as the Federal Emergency Management Agency (FEMA), Small
Business Administration (SBA), or private insurance, has been allocated. The State of North
Carolina prioritizes the first allocation of CDBG-DR funds, in the amount of $198,553,000, forrebuilding homes and restoring the lives of families most impacted by Hurricane Matthew. For
this reason, 84.7% of available funds are dedicated to housing recovery, and providing
supportive services to families with the greatest needs. ”
When talking to homeowner households: The Homeowner Recovery Program will provide
assistance to low and moderate income homeowners who experienced major to severe damage totheir homes and have remaining unmet needs, after subtracting other benefits received from
FEMA, SBA, private insurance, and charitable organizations. The program will include
reconstruction activities, acquisition and construction of new homes, and insurance subsidies toeligible low and moderate income homeowners. North Carolina citizens who were directly
impacted by the disaster will have an opportunity to apply to the Housing Recovery Programs
through one application into the program.

A-13

Appendix 3A: Business Survey
ONE-YEAR POST-HURRICANE MATTHEW FIELD STUDY IN LUMBERTON,NORTH CAROLINA
BUSINESS RECOVERY SURVEYOMB CONTROL #0693-0078; EXPIRATION DATE: 07/31/2019(NIST GENERIC CLEARANCE FOR COMMUNITY RESILIENCE DATACOLLECTIONS)
Date: ______________________
Surveyor(s): ________________________
PIN: _________________
Business Name: ____________________
Address: ___________________________________________________________
1. What is the operational status of this business?
1. Open
2. Permanently closed
3. Moved to alternative location (provide address:______________________________)
4. Not sure/don’t know (take notes on any information that can help us identify the status of
the business:_____________________________________________________________ )(THE FOLLOWING QUESTIONS SHOULD BE ANSWERED BY BUSINESS OWNER
OR MANAGER)
2. What is your role with this business? 1. Owner 2. Manager 3. Owner and Manager
DAMAGE AND BUSINESS INTERRUPTION
3. What kind of physical damage was caused by the flood and how severe was the damage?
Building damage
1. None 2. Minor 3. Moderate 4. Severe 5.
Complete
Contents/inventory damage
1. None 2. Minor 3. Moderate 4. Severe 5.
Complete
Machinery/equipment damage 1. None 2. Minor 3. Moderate 4. Severe 5.
Complete
4. What types of utilities were disrupted at this building? And for how long?
(* N/A: not applicable, if your business does not use this service, please indicate N/A; DK:
don’t know)
Did your business lose
1. Yes 2. No 3. DK 4.
electric power?
N/A
Did your business lose 1. Yes 2. No 3. DK 4.
water?
N/A
Did your business lose 1. Yes 2. No 3. DK 4.
natural gas?
N/A
Did your business lose 1. Yes 2. No 3. DK 4.
sewer?
N/A

If YES, how long until it was fully repaired?
____ Hours or _____ days

still don’t have electricity____ Hours or _____ days

still don’t have water____ Hours or _____ days

still don’t have natural gas____ Hours or _____ days

still don’t have sewer

A-14

Did your business lose
1. Yes 2. No 3. DK 4.
landline phone?
N/A
Did your business lose
1. Yes 2. No 3. DK 4.
cell phone service?
N/A
Did your business lose 1. Yes 2. No 3. DK 4.
Internet/IT?
N/A____ Hours or _____ days

still don’t have landline____ Hours or _____ days

still don’t have cell phone____ Hours or _____ days

still don’t have internet/IT

5. Did this business experience any accessibility problem (i.e. street or sidewalk closure)? 1.
Yes 2. No
6. Immediately following the flood, did you completely cease operation at this location? 1.
Yes 2. No
7. How long did it take for your business to resume operations? ____________ (days)
8. Did your business experience any problem with employee issues?
Employee(s) could not report to work due to transportation problems?
1. Yes 2. No
Employee(s) could not report to work due to the need to fix house?
1. Yes 2. No
Employee(s) could not report to work because their children not yet back to school? 1. Yes
2. No
Employee(s) could not report to work because of disaster-related physical health issues? 1.
Yes 2. No
Employee(s) could not report to work because of disaster-related mental health issues? 1.
Yes 2. No
9. Did this business experience loss of customers?
No

1. Yes (_____% loss of customers) 2.

10. How dependent is this business on physical location? (In other words, can this businessuse virtual location(s) or service(s) during recovery)
1. Not dependent on physical location at all
2. Somewhat dependent on physical location
3. Extremely dependent on physical location
BUSINESS INFORMATION
11. In what year was this business established at this location? ______________ (Year)
12. What is your primary line of business?
1. Construction
2. Manufacturing
3. Retail trade
4. Other (please specify): _______________________________________
13. Before the flood, how many full time and part time employees did this business have?
What about now?
Before:Full time __________Part time ____________
Now:
Full time __________Part time ____________
14. Does this business own or rent the building?
1. Own (including buying the building with mortgage)
2. Rent

A-15

3. Other (please specify) __________________________________
15. What was the business ownership structure before the flood?
1. Single owner
2. Partnership (multiple owners)
3. Corporation or franchise
4. Cooperative
5. Other (please specify): __________________________________
BUSINESS RECOVERY
16. Compared to before the flood, what is the % capacity at which you are now operating?
______ %(For “capacity,” consider aspects of the business that are most important to you, like the quality
and/or quantity of service or product offerings. For example: 50% for reduced capacity, 110%for increased capacity, or 0% for businesses that have not resumed operations.)
17. How has the business revenue changed since the flood? (Please reference grossrevenue.)
1. Decreased greatly
2. Decreased
3. Stay the same
4. Increased
5. Increased greatly
18. How profitable was your business before the flood? What about now?
BeforeNow

Highly profitable Profitable
Highly profitable Profitable

Breaking even Unprofitable
Breaking even Unprofitable

Highly unprofitable
Highly unprofitableclosedclosed

19. Where do you feel your business is in the process of recovery?
1. Still in survival/response mode
2. Recovering
3. Mostly recovered
4. Fully recovered
5. Still in operation but will never recover (please explain)_______________________________
20. Please indicate your level of agreement with the following statements.
We now service more customers outside
our city than we did before the disaster1. Strongly

We now have more suppliers outside ourcity than we did before the disaster1. Strongly

disagreedisagree

2. Disagree 3. Neutral 4. Agree

2. Disagree 3. Neutral 4. Agree

5. Strongly

agree
5. Strongly

agree

A-16

RECOVERY FINANCE
21. Did you have flood insurance coverage on the building, contents, or businessinterruption before the flood? Did you file claims and receive money?
Had
Filed
Received
Received When?
(months
after flood)
Insurance?
Claim?
Money?
Building
1. Yes 2. No
1. Yes 2.
1. Yes 2. No
No
Content
1. Yes 2. No
1. Yes 2.
1. Yes 2. No
No
Business interruption
1. Yes 2. No
1. Yes 2.
1. Yes 2. No
No
22. Did you receive any of the following assistance in recovery?
Assistance Description

Applied?

Received?

a. FEMA financial assistance

1. Yes 2.
No
1. Yes 2.
No
1. Yes 2.
No

1. Yes 2.
No
1. Yes 2.
No
1. Yes 2.
No

1. Yes 2.
No

1. Yes 2.
No

1. Yes 2.
No

1. Yes 2.
No

1. Yes 2.
No
1. Yes 2.
No

1. Yes 2.
No
1. Yes 2.
No

Received When?
(months after flood)b. SBA (Small Business Administration)loanc. Other federal or state funds (specify):______________________________________
d. Local government funds (specify):______________________________________
e. Financial assistance from any church orother NGOs (non-governmentorganizations)?
f. Clean up or repair help from church orother NGOs?
g. Private/bank loans

23. How long can this business function in a deficit (days, weeks, months)?
________________OWNER/MANAGER DEMOGRAPHICS
24. How many years have you worked as a business owner/manager? _____________(years)25. What is your age? ________________ (years)
26. What is your number of years of schooling? Enter number of years _______ and indicate
type of diploma or degree: 1. High School 2. Associate degree

3. Bachelors

4. Masters or higher degree

27. Are you Hispanic? 1. Yes 2. No
28. What is your race?

A-17

1) White
2) Black or African American3) American Indian or Native American4) Asian (Asian Indian, Chinese, Korean, etc.)

5) Native Hawaiian or other Pacific Islander
6) More than one race: ______________
7) Other (Specify): ________________

If you have any comments about the survey and/or business recovery after the flood, pleasewrite them down in the space below.

THANK YOU VERY MUCH FOR COMPLETING THE SURVEY!
This collection of information contains Paperwork Reduction Act (PRA) requirements approvedby the Office of Management and Budget (OMB). Notwithstanding any other provisions of thelaw, no person is required to respond to, nor shall any person be subject to a penalty for failure tocomply with, a collection of information subject to the requirements of the PRA unless thatcollection of information displays a currently valid OMB control number. For this collection, theOMB Control number is:0693-0078 with an expiration date: July 31, 2019. Public reporting
burden for this collection is estimated to be 15 minutes per survey, including the time forreviewing instructions, searching existing data sources, gathering and maintaining the dataneeded and completing and reviewing the collection of information. Send comments regarding
this burden estimate or any aspect of this collection of information, including suggestions for
reducing this burden, to the National Institute of Standards and Technology, Attn: Dr. Jennifer
Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710, telephone 301975-6133, or via email: jennifer.helgeson@nist.gov.

A-18

Appendix 3B: Business Damage States
Lumberton Field Study
Business Damage Survey

Descriptions of Business Damage States

Table 1: Detailed damage descriptions for business building components

A-19

Appendix 3C: Business Consent Script
One-Year Post-Hurricane Matthew Field Study in Lumberton, North Carolina
Business Recovery Survey
Consent Script
OMB CONTROL NO. 0693-0078 Expiration date: 07/31/2019
Hello, my name is (interviewer name) and I am a researcher from (name of university or National
Institute of Standards and Technology) in the (department name/Engineering Laboratory). We areconducting a research study on recovery following the flooding that occurred in Lumberton, N.C. in thedays following Hurricane Matthew in early October last year. We would like to speak with you about how
this event affected your business. In particular, we are interested in learning about the process ofrecovering from the flooding.
This study is part of a larger project led by Center of Excellence for Risk-Based Community
Resilience Planning at Colorado State University. This project is led by Drs. John van de Lindt andBruce Ellingwood, both Professors from the Civil and Environmental Engineering Department and isfunded by the National Institute of Standards and Technology (NIST).
We would like to ask you some brief survey questions about your business’s experience after the flood aswell as some details about your business during this time. Participation will take approximately fifteen
minutes, depending on the experience of your business with Hurricane Matthew. Your participation isvoluntary. If you decide to participate in the study, you may withdraw your consent and stop participation
at any time without penalty.
We will be collecting information about the damage to your business, the repair process, and how theflood disrupted your business’s employees, supply chain, and revenues. When we report and share ourfindings, we will combine the data from all participants into summary statistics and tables so no unique
individual or business can be identified. There are NO KNOWN RISKS or direct benefits to you. We
hope to gain more knowledge on how you and others were affected by Hurricane Matthew and theflooding, so that we can learn from your experiences to help communities better prepare for similar eventsin the future.
So again, we would like to speak with an owner or manager of the business that was here at the timeof Hurricane Matthew and the flooding OR someone who knows about what happened to thebusiness around that time. Would that person be you? you are you willing to participate?
This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the
Office of Management and Budget (OMB). Notwithstanding any other provisions of the law, no person isrequired to respond to, nor shall any person be subject to a penalty for failure to comply with, acollection of information subject to the requirements of the PRA unless that collection of information
displays a currently valid OMB control number. For this collection, the OMB Control number is:06930078 with an expiration date: July 31, 2019. Public reporting burden for this collection is estimated to be
15 minutes per survey, including the time for reviewing instructions, searching existing data sources,
gathering and maintaining the data needed and completing and reviewing the collection of information.
Send comments regarding this burden estimate or any aspect of this collection of information, including
suggestions for reducing this burden, to the National Institute of Standards and Technology, Attn: Dr.
Jennifer Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710, telephone 301975-6133, or via email:jennifer.helgeson@nist.gov.

A-20

Appendix 4A: Public Sector Consent Form
Signed consent form used with School Representatives(verbal consent was used with all other public sector representatives)
Consent to Participate in a Research Study
Colorado State University______________________________________________________________________________
Title of study:
Center of Excellence for Risk-Based Community Resilience Planning
Principal Investigators:
This project is led by Dr. John van de Lindt and Dr. Bruce Ellingwood, both Professors from theCivil and Environmental Engineering Department at Colorado State University. Dr. van de Lindt
can be reached at 970-491-6697 or via email at jwv@engr.colostate.edu and Dr. Ellingwood canbe reached at 970-491-5354 or via email at bruce.ellingwood@colostate.edu.
Who is doing the study?
This five-year project is funded by the National Institute of Standards and Technology (NIST).
Our research team is made up of professors, postdoctoral fellows, and graduate students across
14 universities. Two or more of our field research team members will be interviewing you forthis project.
What is the purpose of this research and why am I being invited to take part in this study?
You have been chosen to be part of this research study because of your experience with the 2016
flooding that occurred in Lumberton, N.C. following Hurricane Matthew. We would like to
speak with you about the choices that you made before, during, and after the flood so we canlearn more about how people responded to and are beginning to recover from the event. Up to
200 people from your community may be invited to be interviewed for this study; however, theteam will begin interviews, initially, with a smaller group of community leaders and key
informants.
What will I be asked to do and how long will it take?
You will be asked to answer questions about what happened before, during, and after the flood.
We are interested in your experiences with preparedness, evacuation, damage, loss, andrebuilding. The interview will be held in a mutually agreeable, private location. With yourpermission, each interview will be audiotaped and will take about 30 minutes of your time. We
would also like to speak to you in the future to learn more about your experiences as they unfold.
Also with your permission, the research team may take photos or videotape of you or your home.
What will it cost me to participate?
There is no cost to you for being part of this study and you will not be paid for your time.
A-21

What are the possible risks, discomforts, and benefits?
It is not possible to identify all potential risks during a research project, but our team has taken
reasonable safeguards to minimize any known and potential risks. The potential risks associatedwith this study are difficult emotions such as anger and sadness. There is no known benefit inparticipating. We hope, however, this will provide a space for reflection and an opportunity tomake a difference for others by sharing your knowledge and experiences.
Do I have to take part in the study?
Your participation in this project is completely voluntary. You may withdraw your consent andstop participating at any time. You have the right to refuse to answer any question(s) for any
reason. You also have the right to refuse to be photographed or audio/video recorded.
Who will see the information that I give?
We will keep private all research records that identify you, to the extent allowed by law.
Anything that you share during our interview will be kept confidential. In addition, your privacy
will be maintained in all written and published documents resulting from this study. However, if
any abuse or illegal activity is discussed, we will have to report that information to theauthorities. Any reports created from this study will use fake names in place of real names ofpeople and organizations.
Other identifying features may be altered as well to protect your confidentiality. Audio files will
be stored in a secure location. They will be marked with an interview number separate from yourname. At the end of the study, all audio files will be erased and all other written materials will bepermanently stored in a secure location. This data will be kept for future use. We may be asked
to share the research files for audit purposes with the CSU Institutional Review Board and the
NIST Human Subjects Protection Office.
If you have questions about this study, you should ask the researcher before you sign this consent
form. If you have questions regarding your rights as a participant, any concerns regarding thisproject, or any dissatisfaction with any aspect of this study, you may contact the Colorado State
University Institutional Review Board at: RICRO_IRB@mail.colostate.edu; or 970-491-1381.
A signed copy of this three-page consent form and Photo/Video-release form will be provided to
you at the time of the interview.
Participant’s Initials ______ Date ______
I agree to be audio recorded for this study (please initial):
YesNo

If you are willing, we may want to conduct 1-2 more interviews with you over the next two years
so that we can follow changes in recovery. We have asked for your address below so that we
may contact you again. I am willing to be contacted again to participate in similar studies related
to disaster recovery (please initial):

A-22

Yes

No

I have read this paper about the study or it was read to me. I know the possible risks andbenefits. I know that being in this study is voluntary. I choose to be in this study. I know that I
can withdraw at any time. I know that it is my choice to be audio taped. I know that any contact
information I provide is optional and will only be used to follow up on the community recovery
process following Hurricane Matthew. I have received, on the date signed, a copy of thisdocument containing two pages.
Signed:

Date:

Name:

Phone:

Address:

Email:_______________________________________
Signature of Research Staff_____________________
Date

Please direct follow-up questions to:
Dr. van de Lindt, Department of Civil Engineering Room A201, Colorado State University, Fort
Collins, CO 80523-1301, 970-491-6697

A-23

Appendix 4B: Semi-Structured Interview Guide for School Representatives
Semi-Structured Interview Guide for School Representatives
Interview Guide[Note: Give interviewee the informed consent form and business card(s) when you first meet]Thank you for meeting with us today. My name is [XX] and my associate is [XX]. We are partof a research team funded by the National Institute of Standards and Technology (NIST), which
is a federal agency within the U.S. Department of Commerce. NIST’s mission is to advancescience, standards, and technology in ways that increase safety and improve our quality of life.
This project is focused on disaster recovery and community resiliency. We are here to learn
about the school district during the one year following the flooding that occurred in Lumberton
as a result of Hurricane Matthew in October of 2016. We will use this information to help build
computer software that communities can use to better prepare for, respond to, and recover fromfuture disasters. Schools are central to our research model, so we thank you for meeting with us
today.
We have a series of questions that we would like to ask you. The interview should take about 30
minutes to complete. Is it okay if I audio record the interview?
Do you have any questions about the interview or the project before we begin?
Interview Questions:

Probes:

Personal and Professional
Information
First, will you please say your name,* What was your role during Hurricane Matthew in 2016?
title, and the name of where you
work?
Impacts
Next, we would like to know a about* How many schools were closed?
the physical and social impacts to the* How many school days were missed?
school district and the students you* How many students were out of school during this time?
educate. Here we would like to get* How many teachers were out of work during this time?
specific data on the following:(Did their pay continue?)(Interviewer: Please ask for access to* What were the overall physical losses (i.e. buildings, text books,
related data if available.)musical instruments, vehicles, technology, etc.)
Response* What information sources did you draw from and how did you
Can you start by telling me how the
keep track of the:Robeson County School District

A-24

responded to the flooding that
occurred in 2016?
Here we are interested in learning
about what kind of information anddata was gathered to inform
decision-making during the response
period of the disaster.
(Interviewer: Please ask for access torelated data if available.)•

location of children•

possible shifts in family status for the child (e.g., parent outof household)•road conditions for safe bus travel•

condition and safety of school buildings•

teacher location and availability•

condition of technology and IT infrastructure•

administrative documents and/or data

* Were there any specific guidelines or thresholds used to decide
when schools would reopen? (e.g. % of staff back, a teacher oradmin in every classroom, minimum teacher-student ratio, %students back, minimum hours of instruction, or maximum hours oftransportation to school)* Did you track students by where they were living (home, hotels,
shelters, with family, etc.)?

* Were there any students who were permanently displaced? How
many?
* Are any students not back in permanent housing? How many?
* Were any special accommodations made for students (orfamilies) who were seriously impacted?
* Are any schools still closed? If so, which ones and for how much
longer?
* What resources did the district use to repair and/or recoverschool buildings and lost supplies such as books, instruments,
technology, district vehicles, etc.?
Recovery* Would you say that there have been specific phases of recovery
Can you please describe the recovery
for schools over the past year? How would you characterize those?
process for Lumberton schools over* What were the main causes of delay for key recovery activities?
the past year?
(e.g. assessing schools, locating staff, repairing and reopening
schools, restoring bus routes, etc.)?
* How would you describe what it would like for the district to be
fully recovered?
* What resources are still needed to achieve a full recovery?
* How are resources being prioritized for school recovery?
* What was the role of the school in terms of family and overall
community recovery?
* What have been the greatest challenges during the recovery
process?

A-25

Policy Connections* Did your school adopt or implement new policies in response to
the flooding? (e.g. waiving/requiring students to make up misseddays of school or changes in testing schedules, prioritizing
restoration of school infrastructure, planning and development forschool placement, safety, and future growth, disaster recovery
resources for schools, etc.)• What was the decision-making process for implementing
this policy?
• How would you describe the long-term outcome of
implementing this policy?

Closing [keep recorder on unless the interviewee seems to be taking an unusually long time andisn’t speaking]This has been exceptionally helpful, and we are grateful for your time. Are there any final
thoughts or comments that you would like to add? Do you have any after action reports or otherresources that you can point me to, to help supplement our investigation?
Ask interviewee to complete contact and demographic information form for reporting
purposes and future follow-up interviews. Also, if respondent promised to share data, ensure thatyou collect that (if hard copy) before you leave. If electronic, follow up as soon as possible after
the interview.
Thank respondent. [turn off recorder]

A-26

Appendix 4C: Semi-Structured Interview Guide for Other Representatives
Semi-Structured Interview Guide for Other Public Sector Representatives
Interview Guide[Note: Read interviewee the informed consent script and provide business card(s) when you firstmeet] Thank you for meeting with us today. My name is [XX] and my associate is [XX]. We arepart of a research team funded by the National Institute of Standards and Technology (NIST),
which is a federal agency within the U.S. Department of Commerce. NIST’s mission is toadvance science, standards, and technology in ways that increase safety and improve our qualityof life.
This project is focused on disaster recovery and community resiliency. We are here to learn
about the decision-making and status of those decisions during the one year following theflooding that occurred in Lumberton as a result of Hurricane Matthew in October of 2016. We
are using this information to inform the development of computer software that communities canuse to better prepare for, respond to, and recover from future disasters. The [facility/system] that[Your agency/organization] has a role in [managing] is an important part of our research model,
and so we thank you for meeting with us today.
We have a series of questions that we would like to ask you. The interview should take about 30
minutes to complete. Is it okay if I take notes from the interview?
Do you have any questions about the interview or the project before we begin?
Interview Questions:
Personal and Professional
Information
First, will you please say your name,title, and the name of where you work?

Impacts
Next, we would like to know a about
the physical and social impacts. Here
we would like to get specific data on
the following:(Interviewer: Please ask for access torelated data if available.)

Probes:[Thank you for your past participation. I’d would like toconfirm some of your personal and professional informationfrom before] What was your role during Hurricane Matthew
in 2016?

❏ Your [agency] has a number of assets, example,
equipment, data records....
❏ [In addition to the assets that you described in ourprevious visit(s) as having lost function], were there
additional assets later recognized as having been beendamaged? (e.g., road failing months later, initially
undetected corrosion, mold, etc.)❏ For each asset, at the time the damage was recognized,
was the expectation that the asset would be replaced?

A-27

❏ Was the time of replacement shorter or
longer than your estimates? cost?
❏ Were there cases in which a decision was made otherthan direct replacement? e.g.,❏ Decision to upgrade instead of replace?
❏ Address need via other means (e.g., cloudservices rather than local data storage).
❏ Your [agency] serves a function to your community.
How would you describe that function?
❏ Prior to the event, what was the size of the population
served within your jurisdiction?
❏ At its peak, what fraction of the population was notserved?
❏ What was considered the last key milestone for recovery
of function that was achieved? (e.g., 90% service, ‘liftingof a water boil advisory’, resumption of normal
operations)❏ What activity or thing ultimately delayed reaching thatmilestone?
❏ What were some earlier key milestones?
❏ Does your [agency] have access to any regularlycollected data that supports analysis of the impact to[your system] from [the event as it evolves over time]?
❏ What is the frequency of the data collection?
❏ Is it publicly available?
❏ How was your [agency] itself impacted, in terms ofstaffing numbers/pay, resources, shift in work duties,
over time since just prior to the event?
❏ How are resources being prioritized for [your agency’s]
recovery and planning for the next event?
❏ What analyses or studies have been conducted in supportof these decisions?
❏ What have been the greatest challenges during therecovery process? What resources are still needed toachieve a full recovery?
❏ How would you describe what it would like for the
district to be fully recovered?

A-28

Policy Connections□ Did your [agency] adopt or implement new policies in

response to the flooding? (e.g. waiving/requiring students
to make up missed days of school or changes in testingschedules, prioritizing restoration of school
infrastructure, planning and development for school
placement, safety, and future growth, disaster recovery
resources for schools, etc.)□ What was the decision-making process for implementing
this policy?

Co-benefits□ Was the recovery planning taken as an opportunity foraddressed other issues?

Closing: This has been exceptionally helpful, and we are grateful for your time. Are there any
final thoughts or comments that you would like to add? Do you have any after action reports orother resources that you can point me to, to help supplement our investigation?
If respondent promised to share data, ensure that you collect that (if hard copy) before you leave.
If electronic, follow up as soon as possible after the interview.
Thank respondent.

A-29

Appendix 5A: Technology in Field Studies
TECHNOLOGY IN FIELD STUDIES
Spatial information support by Google web mapping service
Google mapping services, Google My Maps and Google Street View, were extensively employed
to develop the housing unit and business samples and ultimately to develop a mapping tool that
could be used by field teams to navigate and locate housing units and businesses to conduct
interviews. More specifically Google My Maps provided a platform to create web-accessible,
detailed sample maps of Lumberton, allowing each team member to use their personal internetenabled smartphone to view the locations of their assigned housing units and businesses. The first
field study team members responded positively to using the Google My Maps tool; thus, it was
decided to adopt the tool for the second field study. In general, most team members were familiar
with using Google Maps on their smartphone and found that the Google My Maps tool had a similarinterface. Consequently, the tool only required minimal training yet increased the overall
effectiveness of surveying process.
Before the 2016 survey, the University of Alabama (UA) team created spatially data identifying
the locations of all structures within the study area to develop the housing unit sample for the field
mapping tool. The Texas A&M University (TAMU) team visually confirmed the location of
structures that were identified by the UA team by using Google Street View and Google My Maps
to estimate the “roof top” locations of housing units. Among the final analysis sample from Wave
1, 567 of those housing units were then selected for Wave 2. These spatial data provided the sample
frame for selected primary housing units as described in section 2.2.
To develop the business sample for the field mapping tool, the TAMU team imported businesses
data from the InfoUSA database into ArcMap. The population of businesses that fell within theinundation area or its 100-meter buffer (n = 218) was taken for the sample. To reach the desired
sample size of 350, the team also drew a random sample of businesses outside the inundation area
but within the northern portion of the FEMA 100-year floodplain (n = 240). The sample was thenchecked for geocoding errors and exported into Google My Maps. For more detailed information
about the sample frame, please refer to section 3.2.
For both the household and business surveys, a Google Sheet (i.e., a Google Spreadsheet) was
created. These spreadsheets included details about each housing unit and business such as location,
address, latitude and longitude, and information about the data collection disposition. The
spreadsheets were then imported into Google My Maps, which mapped each point based on its
latitude and longitude. Once Google My Maps were generated, a link to these maps could can beshared providing access using a smartphone or laptop with internet access. If the device also has
GPS, the location of the device can be displayed on the map. Figure A5-1 shows examples of theGoogle My Maps displays. While these are the displays on a PC, the displays on a smart phone
were essentially the same. This feature provides field team members with internet and GPS accesswith additional situational awareness. The satellite image overlay also provides visual evidence
for team members to determine the location of the sampled housing unit and business. Within the
Google My Maps application, a user can select an individual data point to see a list of all of thevariables from the imported spreadsheet.
A-30

Google My Maps allows for the user to define the style of each point based on a variable in the
spreadsheet. For example, the survey status for each housing unit is shown in Figure A5-1. Asteams completed surveys with specific housing units or businesses surveys the spreadsheets were
updated, and then re-imported to update the Google My Maps. The ability to update thespreadsheets allowed the team to stay informed about the location of sampled housing units andbusinesses that were completed or needed to be surveyed. In addition, we noted sample points that
required attention such as possible safety issues, limited access time, and survey form pick up.
Daily reports of progress were recorded and used to plan for the next day, determining who andwhere to visit based on the number of sample points remaining. For example, if only a portion ofsample units were visited in a block, the Google Spreadsheet provided a summary on the percent
complete for the field investigation team to determine a priority level.

Figure 5A-1. Screenshot of Google My Maps – for Lumberton area.
Survey data entry method by online data collection service (Qualtrics)
Qualtrics, a web-based survey data collection and analysis application, was employed to enter datafrom the completed hand-written survey instruments. To analyze the survey data with computerbased statistical analysis tools (such as Stata), data needed to be transferred or converted from thepaper survey forms into a digital format. There were two steps in this data conversion process.
First, the paper survey instrument was recreated into an online digital survey format matching thepaper survey. Second, the results from the paper survey needed to be entered into the online survey.
The University of Kansas and Texas A&M University teams completed the first step, creating theonline version of the surveys using Qualtrics. Figure A5-2. shows an example of the Qualtrics
survey display. At the end of each working day, team members reviewed the surveys they
completed and then transferred their contents into an electronic version using Qualtrics. During
Wave 1, the survey data conversion was conducted weeks after the field study by graduate students
reading scanned Portable Document Format (PDF) versions of the hand-written paper surveys
entering them into an electronic version created using Google Forms. During Wave 2, the data
conversion process was completed in the field by team members that collected the survey data.
A-31

The ability to record survey data in the field enhanced the accuracy and speed of data conversion
process. For example, interviewers imported the survey answers that they had collected only a few
hours earlier. If there was an error such as bad handwriting or inconsistency between answers, theinterviewer could discuss this issue with other interviewers who were present during the survey.
Additionally, the decreased time between data collection and conversion allowed field study teams
to exchange immediate feedback to adapt and ensure accurate and consistent data collection andrecording of responses.
Google Forms is an alternative tool that offers internet-based data collecting and digitizing
methods, and it has some advantages over Qualtrics. The use of Google Forms is free and records
data using Google Sheets which is a widely used spreadsheet format for data sharing. On the other
hand, revising a survey format in Google Forms takes time and can be cumbersome particularly
when there are multiple users. After Wave 1, the Texas A&M University team used Google Forms
to record the PDF scanned survey documents. With the multiple sections of survey questions, it
took a few seconds to scroll down a page in Google Forms. This difficulty was the main reason
that the team decided to use Qualtrics. On the other hand, Qualtrics offers a fast and reliable work
environment, however it is not a free and it requires some training. In addition, Qualtrics alsooffers flexible survey question management tools, and it makes the Qualtrics survey similar to theoriginal paper survey. Mimicking an original paper survey is critical for decrease errors whentransferring the survey data. Qualtrics has Skip Logic and Display Logic that allows forcustomization based on respondent’s answers. By using these survey customization methods, theQualtrics online survey format will correctly show or skip sections based on previous answers likewhether respondents lived in Lumberton before Hurricane Matthew and if respondent’s children
enrolled in school. This ability to adaptively adjust and record the survey data being transferredprovided significant time saving and accuracy benefits compared to using spreadsheet-based tools
like Microsoft Excel and Google Sheets. In general, using Qualtrics increased the effectiveness,
speed, and accuracy of the survey data entry process.

Figure 5A-2. Screenshot of Qualtrics survey.
A-32

Geospatial Data Collection and Storage
The initial reconnaissance mission to Lumberton employed the Extreme Events Web Viewer for
long-term storage and visualization of collected geospatial data. The data were collected in two
formats: digital photography and vehicle-mounted 360° video data. The beta-version web viewer
has been updated to provide visualization for video data and a tool to automatically extract
relevant data from the videos have been created to streamline data collection.
360° video data was collected by mounting cameras to the rooftops of vehicles in the field, as
shown in Figure 5A-3. The video cameras were equipped with on-board GPS with location datalogged typically at 1 location point per second for the videos collected in the recovery study.
This is compared to 360° video data collected without onboard GPS in the initial damage
assessment, in which location data was digitized at 10 second intervals. The location data wasadded to the Extreme Events Web Viewer overlaying a base map of the study area. The videos
are stored in the YouTube channel “Extreme Events Web Viewer”. The video path is displayed inExtreme Events as a line which, when clicked, opens a YouTube video player window within theviewer and shows the video corresponding to the line selected. The video player allows the user
to pan and zoom dynamically as the video plays. The location of the vehicle will update
corresponding to time in the video and at the rate in which GPS data was collected. The
visualization approach to vehicle-mounted 360° video data within the Extreme Events Web
Viewer is shown in Figure 5A-4. The video path is displayed as a green line and the vehicle
location is denoted by a cyan point on the line, called out in the figure with a yellow circle. Thebase map displays aerial imagery showing flooding, taken in the days after Hurricane Matthew
moved through Lumberton. Magenta points denote locations where team members collected
photographs and blue house points denote buildings, as shown in the legend in the top-rightcorner. The building points were digitized previous to the initial damage assessment, and due toflood damage, the buildings have been demolished. The building points store the pre-event
locations of buildings in order to assess changes in longitudinal studies. Building points can also
be used to store all photographs collected of a single building, streamlining the data visualization
process for a building. Allowing relationships
between building points and photographlocations preserves the location where thephotograph was taken, which can be importantwhen assessing which face of the building isshown, but streamlines data viewing when alarge number of photographs are collected for asingle building, as with the Lumberton Middle
School shown in Figure 5A-5 where over 25
photographs were taken of the building.

Figure 5A-3. Vehicle mount setup used to capture 360° video data in Lumberton.

A-33

Figure 5A-4. Vehicle-mounted 360° video visualization in the Extreme Events Web Viewer.

Figure 5A-5. West Lumberton Elementary School with associated photographs.
The collected videos provide a large passive dataset; however, extracting meaningful informationfrom the videos can require a large amount of processing time. To streamline the data collection
process, the Extreme Events Video Capture tool has been created to interface with the Extreme
Events Web Viewer to query data from the viewer, process the data into information, and upload

A-34

the new data to the viewer in a manageable format. The process is carried out through an offline
application which requires input parameters for 360° video selection, video centerline buffer
distance, camera direction relative to direction of travel, and one of multiple processing methods
provided. The tool opens a video stored in the Extreme Events Web Viewer, selects all building
points located within the video centerline buffer distance provided. A geometric function is
executed to select the closest video point location to each building, rotate the video window to thebuilding and extract a photograph from the video. As 360° videos show data by panning theviewing window, the tool must know the proper pan amount for an object of interest to be shownin the view window. This pan function is provided by rotating the video a certain amount of degrees
from the 0° axis of the video. Providing the initial camera direction relative to direction of travel
will calibrate the video so that the 0° axis will be set at the forward direction of travel. This process
is useful as 360° cameras often do not specify which direction is forward, as it typically is not
relevant, and so cameras are often mounted without directionality. When the camera is notmounted correctly, the tool will not know where the 0° axis is set, and the rotation geometry of thetool will be incorrect, leading to extracted photographs which do not include the building ofinterest. The different processing methods provided by the tool allow the user to select whether
they would like to extract a photograph at each building at a 90° angle from the street, looking
straight at the building from the nearest video point, or at oblique angles which can be used captureside profiles of the buildings. Multiple angles can be input to the tool, and multiple photographs
will be extracted if selected. Once all requisite photographs have been extracted from the selectedvideo, the tool allows the user to view the photos for quality assurance and, if desired, add them tothe web viewer. If the user decides to add the photographs to the viewer, the tool will create photo
locations in the Extreme Events Web Viewer, shown as magenta points in Figure 5A-4, at half the
distance between the video centerline location where the photograph was taken and the building
location. Photographs corresponding to each video will be automatically related in the Extreme
Events Web Viewer. Figure 5A-5 shows photograph locations where data extracted from the video
centerline shown in Figure 5A-4 are stored.
Light Detection and Ranging (LiDAR) Point Cloud Data
As documented in the Wave 1 report, much of the flooding in Lumberton occurred due to the gap
in the Lumber River levee system at the CSX railway underpass of Interstate 95. This gap allowed
riverine flooding to surge through the underpass, wash out the railroad tracks and an adjacentaccess road, and flow into the southern region of Lumberton where hundreds of homes andbusinesses were inundated, along with a water treatment plant. Figure A5-6(a) shows the locationsof the river, levee system, intestate, railroad, and underpass. Figure A5-6(b) provides an aerialview of the underpass while flooding was occurring.

A-35

(a)
Lumb
er

CSX
underpass5
I-9

R iver

Levee
CSX

Rail

Line

(b)

Figure 5A-6. CSX underpass where floodwaters entered a portion of the city with (a) abasemap of relevant systems, and (b) aerial photography captured while floodwaters were
flowing through the underpass.
In order to provide robust documentation of the location, a Leica Scanstation C10 laser scanner
was employed to capture high resolution point clouds of the underpass. Multiple point clouds,
including millions of points in three dimensional space, taken from discrete locations in the area
have been registered together to create a single high density virtual representation of the scene.
Multiple locations are necessary when laser scanning as obstructions often limit the information
returned by a single scan. In addition to capturing the high resolution point clouds, the laser
scanner collects photographs of the scene and the color information contained in the images canbe projected onto the point cloud data to provide a more descriptive view of the scene. Figure
5A-7(a) shows the locations where the scans were collected, and Figure 5A-7(b) shows a view of
the underpass point cloud, with image color added to the points. The accuracy of the point cloudis survey-level, therefore measurements taken from the data are highly exact.

A-36

(a)

Scan Locations(b)

Figure 5A-7. LiDAR scans of the CSX underpass in Lumberton, with (a) locations wherescans were collected, and (b) an image of the point cloud.

A-37

Appendix 5B: Safety Protocols
SAFETY PROTOCOLSNIST Disaster and Failure Studies Safety Protocols
The National Institute of Standards and Technology promotes a culture of safety across the agency.
The Office of Safety, Health, and Environment (OSHE) leads and supports NIST in carrying out
their mission safely. OSHE’s vision is for safety to be an integral core value and vital part of NIST
culture. The Divisions and Laboratories hold quarterly and annual safety meetings, respectively,
to disseminate safety materials and best practices. The Disaster and Failure Studies (DFS) Programin the Materials and Structural Systems Division (Engineering Laboratory) follows standard
operating procedures for all field deployments, including the Lumberton longitudinal field
campaigns. Deployments following disaster events constitute working in an unstructured
environment with possibly changing exposures to hazards. Therefore, awareness and avoidance ofpotential hazards are the greatest tools for mitigating risk in the field.
First-Level Hazard Review: NIST personnel operating in the field must be alert to the potential
hazards present in the immediate environment and take the appropriate steps to mitigate or
eliminate the risk posed by a hazard. A first-level hazard review (FLHR) for the DFS Program is
updated and reviewed regularly by a panel of experts to identify common hazards (e.g., downed
power lines, damaged structures, debris, heat stress, violence, etc.) that are encountered during
field work and associated mitigation controls (e.g., personal protective equipment, training
courses, first-aid kits, etc.).
Personal Protective Equipment: NIST personnel are responsible for acquiring, storing, andcarrying personal safety equipment (e.g., steel-toed safety shoes, eye protection, and safety vests)
that are required to comply with the FLHR. The DFS Operations Center in Gaithersburg, MD
stocks PPE (e.g., hard hats, gloves, dust masks, etc.) and other supplies (e.g., first-aid kits,
flashlights, hand sanitizer, etc.) that may be needed for a broad range of hazard missions. NIST
personnel that are missing any PPE before deployment are required to visit the DFS Operations
Center to comply with all mitigation controls required for the specific deployment.
Table-Top Exercise: A table-top exercise, simulating typical DFS activities was developed toreview existing safety protocols, identify gaps in the safety protocols, and recommend potentialchanges to the FLHR. The most recent DFS table-top exercise was held at NIST in Gaithersburg,
MD on January 3, 2018. The participants included the Engineering Laboratory’s Safety Officer,
the DFS Director, EL Group Leaders and Division Chief, and DFS deployment team leaders. The
exercise began with a review of DFS emergency procedures and the roles of the individuals in theroom during a typical deployment. The scenarios practiced during the exercise included: (1)interacting with members from other federal agencies, the private sector, and the academic sector
in the field; (2) accessing damaged buildings to collect data; and (3) interacting with the public in
the field. The outcome of the table-top exercise was a list of best practices for safety in the fieldwith some recommendations for minor updates to the FLHR.

A-38

Tailored SOP to Specific Deployments: Since DFS deployments and associated risks vary by
hazard type (e.g., wildland-urban interface fires, building fires, hurricanes, floods, windstorms,
earthquakes, and construction failures), safety standard operating procedures (safety SOP) arecreated for each deployment. The tailored safety SOP one-pager includes training, safety
requirements, supplies, and PPE required by the specific deployment. This one-pager is reviewed
and discussed with the entire team during a safety briefing at NIST prior to deployment. During
the deployment, NIST personnel meet prior to each day’s field activities to discuss the technicalactivities for the day, to discuss the anticipated hazards, to review the safety SOP, to inventory thesafety-related equipment, and to decide upon the time and location the team will reconvene
throughout the day. An example of a safety SOP is provided below.
Safety Standard Operating Procedures for a Windstorm Preliminary Assessment
Deployment
Training - Complete all required safety training before deployment, including tetanus shots
Safety Requirements● Designate a field safety officer for each team or sub-team.
● Always use buddy system and travel in no less than pairs, maintaining visual contact.
● Cell phone for each person. Have phone numbers for all team members available andGaithersburg contacts.
● Consider using walkie-talkies for team communication. If the team splits up into smaller
teams are in area, also consider routine radio/communication checks with other teams
and/or virtual team members when field teams change locations.
● Hold daily morning briefings that include safety.
● Know the location of the nearest local hospital, police station and fire department.
● Don’t walk in areas which have standing water.
● Structurally damaged buildings – Do not enter, stay 1.5 times height of building awayfrom severely damaged buildings.
● Know where hazardous waste sites and damaged chemical plants are located and do notenter.
● Downed power lines watch out for them and stay away. Do not drive over them.
● Driver should be focused on driving and have the assistance of a dedicated navigator –signs / landmarks may be damaged or missing and driver should be scanning for debris
and pedestrians.
● Before and after entering a structure, record names, location, and time (consider Slack),
and/or have team members outside structure.
● If possible, clearly identify your team as working damage assessment teams – NIST logo,
hard hat, reflective vest, and badge are all helpful.
● Be aware of heavy construction equipment near you.
● Wash or disinfect hands after removal of PPE.
Supplies● Insect repellant with at least 25% DEET.
● First aid kit (at least one per vehicle).
A-39

●
●
●
●
●
●
●

Clorox disinfectant wipes (for shoes and clothing)
Plastic bags for contaminated shoes and clothing.
“Tekwipes” or similar to keep hands clean and free of grease/oils
SunscreenFlashlight
Personal medications.
NIST badge and/or Center identification

Personal Protective Equipment (PPE)● Hard hat● Safety glasses with side shield● Safety shoes (ASTM conformance) with puncture resistant soles or puncture resistantinserts for personal shoes (with closed toes).
● Reflective safety vests.
● N95 disposable respirators for voluntary use. Keep several in a clean, plastic bag until
needed.
● Disposable ear plugs for hearing protection. Keep several in a clean, plastic bag until
needed.
● Gloves (suede or leather and nitrile).
● Disposable coveralls.

A-40

Appendix 5C: Training Protocol
FIELD TEAM TRAINING PROTCOLThe Center developed a Survey Training Guide to ensure uniform field protocols across all
members deploying to collect post-disaster data. The Survey Training Guide is provided below
and includes: (1) background of the surveys being administered during the deployment; (2)comprehensive description of the role of the interviewer; (3) a safety policy for field teams inspired
by Paterson et al.’s (1999) protocol for researcher safety; (4) moving in the community; (5) achecklist of required survey materials; (6) best practices for approaching respondents; and (7) data
management plans. These protocols are reviewed and discussed with the entire team during a webbased training prior to deployment. During the deployment, Center personnel shall meet prior toeach day’s field activities to discuss the technical activities for the day, to discuss the anticipatedhazards and best practices for survey work, to review the safety and surveying protocols, toinventory the safety-related equipment, and to decide upon the time and location the team willreconvene throughout the day.

A-41

SURVEY TRAINING GUIDEBACKGROUND OF THE SURVEY AND YOUR ROLE AS THE INTERVIEWER:□ Overview of Interviewer Responsibilities*The cardinal rule of interviewing is to remember you are acting as an objective recorder of eachparticipant’s experiences. Your personal beliefs and judgments have no place in the interview
setting if you expect to do your job. As difficult as it may sound, leaving your convictions andcriticisms at the door is imperative to the success of the interview. It doesn’t matter how thesituation appears to you, showing disdain or contempt will lead to refusals. You see this person
at one brief moment in his/her life, and his/her story is far longer than we could record in thetime allotted for a survey.
*The second principle for successful interviewing is to thoroughly know the purpose of the
project. Your confidence in the work and your abilities will be as apparent as the light of day. In
the first 15 seconds of your encounter, potential participants decide whether or not they want tocontinue to be a part of the study. If you don’t know why you are there, or what your efforts will
yield neither will the respondents. This first impression is very difficult to change. Know theCenter’s and the field study’s purpose and know their value before you knock on the door.

□ Handling of Confidential Information and the Oath of Confidentiality□ Introduction to Interviewing Techniques□ How to Conduct a Survey*Ask all questions on the form unless you have been instructed on the form to “skip.” If you
know the answer to a question (e.g. because you know the Respondent or because you haveheard the information via casual conversation with him or her before the interview), confirm theanswer with the Respondent and record the answer in the proper place. Blanks left on forms
cannot be coded for evaluation research and the Respondent’s story - his/her needs, challenges,
and successes, would be lost.
*Ask all questions in the order in which they appear in the form. The questions follow one
another in a sequence that has been established by researchers; any changes can destroy theintent of the questions. Once established, the order of questioning must be preserved.
*You must be alert to interviewer instructions. Text that is to be spoken - questions to be askedof the respondent - are in bold type, while instructions to the interviewer are always in italics
and often in [square brackets].
*You must follow instructions as to which parts of a question need to be asked in what order.
You must follow “skip to” instructions. Following skips is necessary not only for accuracy of
information gathering, but will make the interview go faster. Ignoring skips can be very
annoying to respondents.

A-42

*The goal is to accurately record the Respondent’s answers to the questions. Never interpret a
Respondent’s answer—instead, probe to get him or her to clarify what s/he means. Further
explanation on proper probing techniques is supplied in the manual.
For numeric responses, it is important that the answer is recorded properly in the space provided.
Be aware of the units used in a question, for example, if the question is asking how many weeks;
be sure to enter weeks or fractions of weeks, not the number of days.
“Don’t know” and “not sure” are legitimate answers, but they should be avoided. Often withsome assistance the interviewer can help the Respondent clarify his or her answer to fit within acategory given (see probing hierarchy). If after probing and attempting to assist the respondents
in remembering, the answer is still, “Don’t know,” be sure to record this. Do not leave thequestion blank.

□ Safety Policy for Field Teams
1. The number 1 safety rule is: Trust your instincts! If you feel uncomfortable about going
into any area, or into a building, or an elevator, do not go there. If you feel uncomfortable
about being in an area or inside a building or a respondent's apartment or other living
space, leave immediately.
2. Carry important phone numbers with you everywhere. If something emerges and you
need help, call one of the Team Leads.
3. The general rule is: Check it out first. Be alert to the type of people and activities in
the area, the location of the buildings, trailers or rooms you will enter.
4. You will be working in pairs or teams.
5. Some interviews or cases may require two team members to be present for the interview.
One will act as an escort while the other interviews the respondent. Since privacy isimportant for conducting the interview, the escort should try to find a space within theapartment, trailer, house or interview setting that is away from the actual interview. Field
staff must use their own judgment.
6. Develop a code or signal to use with your team that will alert others of a potentially
dangerous situation. You must practice this before entering the field together. Develop aword or phrase to indicate that you should not enter or that you should leave an area orthat you should leave a respondent’s household. This signal is used to discontinue aninterview that is in progress if this is warranted by safety considerations.
7. Check in when you have completed your work in the field for the day.
8. Never enter a building whose entrance is blocked by a group of people or objects. Neverenter a building where there is apparent police activity.

A-43

9. Before beginning an interview in a respondent's home, take a moment to survey thesituation. Know where the entrances and exits are. Do not allow the respondent to lockthe door from the inside and remove the key. You can give some reason such as, “myescort/team members are here so it is not necessary, and s/he may have to leave for a bit
to report to the office. I wouldn't want him to disturb our interview.” If respondent oranyone in the household wants to lock the door from the inside, a team member should
definitely stay with you.
10. If there is an elevator in the building, use it - even to go up or down one floor. However,
do not enter an elevator with someone who appears suspicious. When you use stairways,
listen first to see if anyone else is there. If you hear others, you and your escort should
talk with each other to let others know that there are two of you (casual conversation,“how 'bout those Hornets?”). Of course, if the sounds make either of you uncomfortable,
use your exit signal and leave.
12. Wearing your NIST Center shirt will help to identify you to participants. Otherwise yourattire should be casual, but professional. Do not visibly carry valuables or display large
amounts of money. You should not carry a small purse that looks made to carry money
but rather a large bag that looks like it contains work materials. Canvas bags are good.
13. Always carry your ID and a small amount of cash in case of an emergency.
14. If you are going into dark halls or stairways bring a flashlight. If you are working atnight, field staff should always carry a flashlight for poor lit areas. In an emergency
situation you will attract the most attention by yelling "FIRE!" repeatedly as loud as you
can. In an emergency situation, you should dial 911 immediately to get assistance andthen contact project staff.
15. If you are carrying a handheld device for data collection, keep it in a nondescript bag. If
someone challenges you for the equipment, hand it over. Your safety is more important
than the tablet!
A. Moving in the Community:
ALWAYS wear your shirt and ID badge so that it can easily be seen. This will give peoplesome sense of why you are there.
1. If there are "block watchers" in the neighborhood, speak to them. These are folks whotake it upon themselves to watch out for strangers and possible misdeeds on the block. If
anyone informs you that you are not to proceed into a given area or apartment, take theiradvice. Report this to your Team Leads.
2. Act like you know where you are going, as if you belong. It must be clear that you are
there for a purpose and you know what you are doing, not that you are lost and stumblingaround.
□ Size of Field Teams (2-4 persons per team)

A-44

SURVEY, SAMPLING, AND IRB:

□ Review of Sample□ Introduction to Consent and Survey Instrument□ Field Protocol□ Interviewer Checklist – List of Materials that All Survey Interviewers Need to Have
*surveys*mental health / resource referral form
*ink pens*legal size clipboard*shirt / badge*car magnet
QXQ:□ Review and Practice Questions with Family / Friends / Colleagues□ Send Revisions / Comments to Survey Leads□ Send “How Long it Took to Do the Survey” to Team Leads
APPROACHING RESPONDENTS:□ Script for Approaching Respondents: “My name is X and this is X. We are here with a brief
survey regarding Hurricane Matthew and its effects. It will take about X minutes tocomplete. You were selected through a random process to represent your larger community.
Do you have time to answer these questions now?”*Hand respondent information sheet.
□ Convincing a Respondent to Talk to You*Show Confidence*Smile*Express Enthusiasm*Underscore Importance of the Study□ Converting a Soft Refusal*Don’t Take a Soft No for a Hard No!
*Emphasize that the Survey is Brief*Underscore Importance of Participation□ What to do after a Hard Refusal – Replacement protocol*What is a Hard NO? (Door slammed in face. Get off my porch. NO don’t come back… These
are hard NO’s and you should take them as a No.)
*Replacement protocol…

A-45

□ What to do if Someone Doesn’t Answer Door (but it looks like they are back living in thehome)□ Coding Sheet□ End of the Interview*After the survey, thank the respondent for his/ her time.

*Give the respondent a copy of the Resource sheet. If you forgot to give them the brochure, dothat now.
DATA:□ After the interview, save all paper surveys□ Upload paper surveys back at the hotel

A-46

FIELD STUDY TEAM AFFILIATIONThis publication is available free of charge from: https://doi.org/10.6028/NIST.SP.1230-2

LAUREN BADEAUXJENNIFER TOBIN

University of Kansas

University of Colorado, Boulder

ANDRE BARBOSAJOHN W. VAN DE LINDT

Oregon State University

Colorado State University

SHANE CRAWFORDMARIA WATSON

University of Alabama

Texas A & M University

MARIA DILLARD

KAI WU

National Institute of Standards and Technology

Texas A & M University

DONGHWAN GU
Texas A & M University
SARA HAMIDEH
Iowa State University
KENNETH HARRISON
National Institute of Standards and Technology
JENNIFER HELGESONNational Institute of Standards and Technology
SAYMA KHAJEHEI
Iowa State University
MARIA KOLIOU
Texas A & M University
MEHRDAD MEMARI
Colorado State University
JUDITH MITRANI-REISER
National Institute of Standards and Technology
KIJIN SEONG
Texas A & M University
ELAINA SUTLEY
University of Kansas